Showing 1–50 of 92 results for author: Jesse, S

Dynamics of Null and Electrostatic Blind Spots for Quantitative PFM

Authors: Marti Checa, Holland Swicegood, Ruben Millan-Solsona, Ralph Bulanadi, Jerry Zhao, Jack Lasseter, Stephen Jesse, Liam Collins

Abstract: Piezoresponse force microscopy is a cornerstone technique for probing nanoscale electromechanical phenomena, yet quantitative and in some cases qualitative interpretation remains hindered by parasitic electrostatic forces coupled through cantilever dynamics. Recent approaches aim to suppress these artifacts by operating at resonance defined null spots or electrostatic blind spots, but whether thes… ▽ More Piezoresponse force microscopy is a cornerstone technique for probing nanoscale electromechanical phenomena, yet quantitative and in some cases qualitative interpretation remains hindered by parasitic electrostatic forces coupled through cantilever dynamics. Recent approaches aim to suppress these artifacts by operating at resonance defined null spots or electrostatic blind spots, but whether these conditions are equivalent and how they evolve under realistic measurement conditions has remained unclear. Here, combining analytical beam models, geometrically faithful finite-element simulations, and automated interferometric measurements, we show that NS and ESBS are fundamentally different operating conditions. The NS is a modal zero at contact resonance where sensitivity to all excitations vanishes, whereas the ESBS is a quasistatic position where only the distributed electrostatic response is suppressed. Automated measurements reveal that both conditions evolve with the tip sample boundary condition yet remain spatially separated under realistic experimental conditions. While beam models capture the dominant cantilever mechanics, finite-element simulations and experiment show that quantitative prediction of near tip behavior requires realistic three dimensional probe electrostatics and mechanics. These findings establish NS and ESBS as dynamic operating conditions and provide a practical framework for advancing interferometric PFM toward truly quantitative electromechanical metrology. △ Less

Submitted 29 July, 2026; originally announced July 2026.

Comments: 22 pages, 5 figures

AEcroscopyWave: Towards Self-Driving Characterization Platforms for Agentic AI

Authors: Yongtao Liu, Jawad Chowdhury, Ganesh Narasimha, Ralph Bulanadi, Liam Collins, Ruben Millan Solsona, Marti Checa, Asraful Haque, Sumner B. Harris, Stephen Jesse, Rama Vasudevan

Abstract: The characterization of electronic materials has traditionally been stratified into two distinct regimens: industry-scale automated systems to inspect materials for defects and ensure quality (such as in the semiconductor industry), and highly customized, operator-driven systems requiring human experts. The former offers high throughput but limited flexibility, whereas the latter is heavily bandwi… ▽ More The characterization of electronic materials has traditionally been stratified into two distinct regimens: industry-scale automated systems to inspect materials for defects and ensure quality (such as in the semiconductor industry), and highly customized, operator-driven systems requiring human experts. The former offers high throughput but limited flexibility, whereas the latter is heavily bandwidth-limited but provides research-grade discovery capabilities. Recent advances in "self-driving" characterization tools offer the potential to bridge the two stratified regimes, by the creation of application program interfaces (APIs) that can control hardware, and the integration of AI methods to incorporate autonomy into the process. Here, we discuss our latest developments in AEcroscopyWave, a custom-built characterization platform for the agentic-AI era, that provides unified control of scanning probe microscopes with programmable peripheral instrumentation, highlighting the design choices that are necessary for maximizing the capability of the system and the ease of use for both human and AI agents. The benefits of making heterogeneous scientific instruments accessible, composable and usable by agents is demonstrated by test cases. △ Less

Submitted 24 July, 2026; originally announced July 2026.

Comments: 17 pages, 5 figures

Foundry CMOS platform for multimodal quantum materials characterization

Authors: Sharad Kumar Yadav, Luca Nessi, Ondrej Dyck, Jinchen Wang, Bogdan Dryzhakov, Alex Melendez, Huan Zhao, Qian Song, Doha Amer, Cole Brabec, Saleh Alqazlan, Ruonan Han, Riccardo Comin, Stephen Jesse, Dirk Englund, Jawaher Almutlaq

Abstract: Quantum materials experiments increasingly rely on microwave, electrical, thermal, optical, and structural probes, but these capabilities are typically assembled from custom hardware that limits reproducibility and scalability. Here we show that a commercial 65-nm CMOS process can be repurposed as a passive, foundry-manufacturable characterization platform by functionally partitioning its metal st… ▽ More Quantum materials experiments increasingly rely on microwave, electrical, thermal, optical, and structural probes, but these capabilities are typically assembled from custom hardware that limits reproducibility and scalability. Here we show that a commercial 65-nm CMOS process can be repurposed as a passive, foundry-manufacturable characterization platform by functionally partitioning its metal stack into microwave, thermal, and electrical subsystems within a 1 mm2 footprint. The integrated RF architecture enables cryogenic magnetic susceptibility measurements of Fe3GeTe2 heterostructures at 1.75 K without sample-specific fabrication. We further demonstrate NV-center optically detected magnetic resonance (ODMR) with >20% contrast at 4-9 dBm microwave power, reducing power requirements by 20-25 dB relative to conventional antenna-based approaches while maintaining sensitivities of 2-3 uT/sqrt(Hz). We additionally confirm compatibility with in-situ electron-beam imaging, showing no measurable degradation in image quality upon device operation. These results establish a scalable, foundry-manufacturable platform for multimodal quantum sensing and materials characterization. △ Less

Submitted 20 July, 2026; originally announced July 2026.

Inference-Sufficient Representations for High-Throughput Measurement: Lessons from Lossless Compression Benchmarks in 4D-STEM

Authors: Ondrej Dyck, Andrew R. Lupini, Albina Borisevich, Miaofang Chi, Rama K. Vasudevan, Stephen Jesse

Abstract: Four-dimensional scanning transmission electron microscopy (4D-STEM) generates multi-gigabyte datasets, creating a growing mismatch between acquisition rates and practical storage, transfer, and interactive visualization capabilities. We systematically benchmark 13 lossless compression implementations across 5 representative datasets (8~MiB to 8~GiB, 49.5--92.8\% sparsity), with 10 independent run… ▽ More Four-dimensional scanning transmission electron microscopy (4D-STEM) generates multi-gigabyte datasets, creating a growing mismatch between acquisition rates and practical storage, transfer, and interactive visualization capabilities. We systematically benchmark 13 lossless compression implementations across 5 representative datasets (8~MiB to 8~GiB, 49.5--92.8\% sparsity), with 10 independent runs per method. HDF5 provides built-in gzip compression, of which gzip-9 typically achieves the highest compression ratio but is slow. We therefore evaluate widely available alternatives (via hdf5plugin), including the Blosc family. As a representative comparison, blosc\_zstd achieves compression comparable to gzip-9 (mean 13.5$\times$ vs 12.3$\times$) while compressing 19--69$\times$ faster and reading 1.9--2.6$\times$ faster across datasets. Compression ratios are deterministic, and timing measurements are highly reproducible (CV $<$2\%). Compression performance follows a power law with sparsity ($R^2 = 0.99$), ranging from 5$\times$ for moderately sparse data to 35$\times$ for highly sparse data. We identify six top-performing implementations optimized for different use cases and demonstrate that 4D-STEM data can be routinely compressed by $>$10$\times$. While these results provide practical guidance for lossless compression selection, the broader conclusion is that lossless compression preserves measurements but does not by itself guarantee sustainable high-throughput workflows. As detector rates rise, data handling will increasingly require inference-driven representations -- i.e., deciding what must be preserved to support a scientific inference, rather than defaulting to storing fully dense raw measurements. △ Less

Submitted 26 March, 2026; originally announced April 2026.

Atomically Precise Electron Beam Sculpting of Bilayer h-BN: The Role of Crystallographic Orientation and Milling Strategy

Authors: Ondrej Dyck, Andrew R. Lupini, Ivan Vlassiouk, Matthew Brahlek, Rob Moore, Stephen Jesse

Abstract: Achieving atomic precision in top-down manufacturing remains a fundamental challenge nanofabrication technology. Here, the focused electron beam of a scanning transmission electron microscope is used to demonstrate atomically precise sculpting of hexagonal boron nitride (h-BN) bilayers, achieving nanoribbons as narrow as 6 Å with atomically smooth edges. The key to this precision lies in understan… ▽ More Achieving atomic precision in top-down manufacturing remains a fundamental challenge nanofabrication technology. Here, the focused electron beam of a scanning transmission electron microscope is used to demonstrate atomically precise sculpting of hexagonal boron nitride (h-BN) bilayers, achieving nanoribbons as narrow as 6 Å with atomically smooth edges. The key to this precision lies in understanding how the underlying atomic structure, particularly in twisted bilayer systems, influences the milling process. High-angle annular dark-field imaging combined with multislice simulations reveals distinct intensity signatures that allow identification of different stacking arrangements within moiré patterns. Mathematical analysis of moiré lattices provides a predictive framework for determining optimal cutting directions, with cuts along armchair directions yielding superior edge quality compared to zigzag orientations. Surprisingly, a sequential milling approach, where a small electron beam subscan area is translated during the process, produces significantly better results than parallel milling of the entire target region. To understand these differences we implemented a stochastic milling model that reveals that sequential milling minimizes unwanted exposure to surrounding material through beam tail effects. These findings establish a framework for achieving atomic precision in electron beam sculpting of two-dimensional materials and provide fundamental insights applicable to the broader challenge of top-down nanofabrication. △ Less

Submitted 18 February, 2026; originally announced February 2026.

Auto-3DPFM: Automating Polarization-Vector Mapping at the Nanoscale

Authors: Ralph Bulanadi, Marti Checa, Michelle Wang, Franck Rothen, John Lasseter, Sumner B. Harris, Daniel Sando, Valanoor Nagarajan, Liam Collins, Stephen Jesse, Rama Vasudevan, Yongtao Liu

Abstract: The functional properties of ferroelectric materials are strongly influenced by ferroelectric polarization orientation; as such, access to consistent and precise characterization of polarization vectors is of substantial importance to ferroelectrics research. Here, we develop a fully automated three-dimensional piezoresponse force microscopy (Auto-3DPFM) technique automating all essential steps in… ▽ More The functional properties of ferroelectric materials are strongly influenced by ferroelectric polarization orientation; as such, access to consistent and precise characterization of polarization vectors is of substantial importance to ferroelectrics research. Here, we develop a fully automated three-dimensional piezoresponse force microscopy (Auto-3DPFM) technique automating all essential steps in interferometric PFM for 3D polarization vector characterization, including laser alignment, tip calibration and approach, image acquisition, polarization vector reconstruction, and visualization. The automation reduces the experimental burden of ferroelectric polarization vector characterization, while the back-and-forth calibration ensures consistency and reproducibility of 3D polarization reconstruction. An algorithmic workflow is also developed to identify domain walls and calculate their characteristic angles via a spatial vector-angle-difference method, presenting one unique capability enabled by Auto-3DPFM that is not accessible with traditional PFM techniques. Beyond representing a significant step forward in 3D polarization mapping, Auto-3DPFM promises to accelerate discovery via high-throughput and autonomous characterization in ferroelectric materials research. When integrated with machine learning and adaptive sampling strategies in self-driving labs, Auto-3DPFM will serve as a valuable tool for advancing ferroelectric physics and microelectronics development. △ Less

Submitted 9 December, 2025; originally announced December 2025.

Nanosculpting lateral weak link junctions in superconducting Fe(Te,Se)/Bi2Te3 with focused Si++ ions and implications on vortex pinning

Authors: Debarghya Mallick, Sujoy Ghosh, An Hsi Chen, Qiangsheng Lu, Liam Collins, Sangsoo Kim, Gyula Eres, Ivan Kravchenko, Stephen Jesse, Steven J. Randolph, Scott T. Retterer, Matthew Brahlek, Robert G. Moore

Abstract: Superconductor-normal-superconductor (SC-N-SC) weak links enable Cooper-pair tunneling and serve as Josephson junctions (JJs) used in modern superconducting qubits. Conventional JJs rely on vertically stacked Al-AlOx-Al trilayers that are difficult to fabricate and are sensitive to ambient exposure. Here, we demonstrate an all-in-plane alternative by "nanosculpting" ~100 nm-wide channels into thin… ▽ More Superconductor-normal-superconductor (SC-N-SC) weak links enable Cooper-pair tunneling and serve as Josephson junctions (JJs) used in modern superconducting qubits. Conventional JJs rely on vertically stacked Al-AlOx-Al trilayers that are difficult to fabricate and are sensitive to ambient exposure. Here, we demonstrate an all-in-plane alternative by "nanosculpting" ~100 nm-wide channels into thin films of FeTe0.75Se0.25/Bi2Te3 (FTS/BT), a candidate topological superconductor, with a Si++ focusses ion beam (FIB). Systematic irradiation shows that increasing the ion dose, while keeping the beam energy constant, progressively suppresses both the critical temperature (Tc) and critical current (Ic), confirming the creation of a controllable weak link even though a Fraunhofer interference pattern is not observed. Kelvin prove force microscopy , atomic force microscopy and scanning electron microscopy corroborate the structural and electronic modification of the irradiated region. Ic (B) measurements reveal a slower field-induced decay of Ic at higher doses, indicating that irradiation-induced defects act as vortex-pinning centers that mitigate vortex motion and associated dissipation, By tuning beam energy and dose, the process shifts from SC-N-SC regime toward a superconductor-insulator-superconductor (SC-I-SC) geometry, offering a simple scalable pathway to JJ fabrication. These results established FIB pattering as a versatile platform for engineering robust, scalable fault-tolerant qubits. △ Less

Submitted 12 September, 2025; originally announced September 2025.

Autonomous Fabrication of Tailored Defect Structures in 2D Materials using Machine Learning-enabled Scanning Transmission Electron Microscopy

Authors: Zijie Wu, Kevin M. Roccapriore, Ayana Ghosh, Kai Xiao, Raymond R. Unocic, Stephen Jesse, Rama Vasudevan, Matthew G. Boebinger

Abstract: Materials with tailored quantum properties can be engineered from atomic scale assembly techniques, but existing methods often lack the agility and accuracy to precisely and intelligently control the manufacturing process. Here we demonstrate a fully autonomous approach for fabricating atomic-level defects using electron beams in scanning transmission electron microscopy (STEM) that combines advan… ▽ More Materials with tailored quantum properties can be engineered from atomic scale assembly techniques, but existing methods often lack the agility and accuracy to precisely and intelligently control the manufacturing process. Here we demonstrate a fully autonomous approach for fabricating atomic-level defects using electron beams in scanning transmission electron microscopy (STEM) that combines advanced machine learning and automated beam control. As a proof of concept, we achieved controlled fabrication of MoS-nanowire (MoS-NW) edge structures by iterative and targeted exposure of $MoS_2$ monolayer to a focused electron beam to selectively eject sulfur atoms, utilizing high-angle annular dark-field (HAADF) imaging for feedback-controlled monitoring structural evolution of defects. A machine learning framework combining a random forest model and convolutional neural networks (CNN) was developed to decode the HAADF image and accurately identify atomic positions and species. This atomic-level information was then integrated into an autonomous decision-making platform, which applied predefined fabrication strategies to instruct beam control about atomic sites to be ejected. The selected sites were subsequently exposed to localized electron beam using an FPGA-controlled scan routine with precise control over beam positioning and duration. While the MoS-NW edge structures produced exhibit promising mechanical and electronic properties, the proposed autonomous fabrication framework is material-agnostic and can be extended to other 2D materials for the creation of diverse defect structures and heterostructures beyond $MoS_2$. △ Less

Submitted 1 July, 2025; originally announced July 2025.

Comments: 19 pages, 5 figures

Evidence of chiral fermion edge modes through geometric engineering of thermal Hall in $α$-RuCl$_3$

Authors: Heda Zhang, Gabor B. Halasz, Sujoy Ghosh, Stephen Jesse, Thomas Z. Ward, David Alan Tennant, Michael McGuire, Jiaqiang Yan

Abstract: The experimental observation of half-integer-quantized thermal Hall conductivity in the Kitaev candidate material $α$-RuCl$_3$ has served as smoking-gun signature of non-Abelian anyons through an associated chiral Majorana edge mode. However, both the reproducibility of the quantized thermal Hall conductivity and the fundamental nature of the associated heat carriers, whether bosonic or fermionic,… ▽ More The experimental observation of half-integer-quantized thermal Hall conductivity in the Kitaev candidate material $α$-RuCl$_3$ has served as smoking-gun signature of non-Abelian anyons through an associated chiral Majorana edge mode. However, both the reproducibility of the quantized thermal Hall conductivity and the fundamental nature of the associated heat carriers, whether bosonic or fermionic, are subjects of ongoing and vigorous debate. In a recent theoretical work, it was proposed that varying the sample geometry through creating constrictions can distinguish between different origins of the thermal Hall effect in magnetic insulators. Here, we provide experimental evidence of chiral fermion edge modes by comparing the thermal Hall effect of a geometrically constricted $α$-RuCl$_3$ sample with that of an unconstricted bulk sample. In contrast to the bulk crystals where the thermal Hall signal fades below 5\,K, the constricted crystals display a significant thermal Hall signal that remains measurable even at 2\,K. This sharp difference agrees well with the theoretical prediction and provides compelling evidence for the contribution of chiral fermion edge modes to the thermal Hall effect in $α$-RuCl$_3$. More broadly, this work confirms that the geometry dependence of the thermal Hall effect can help identify chiral spin liquids in candidate materials like $α$-RuCl$_3$ and paves the way for the experimental realization of thermal anyon interferometry. △ Less

Submitted 8 May, 2025; originally announced May 2025.

Probing Boron Vacancy Defects in hBN via Single Spin Relaxometry

Authors: Alex L. Melendez, Ruotian Gong, Guanghui He, Yan Wang, Yueh-Chun Wu, Thomas Poirier, Steven Randolph, Sujoy Ghosh, Liangbo Liang, Stephen Jesse, An-Ping Li, Joshua T. Damron, Benjamin J. Lawrie, James H. Edgar, Ivan V. Vlassiouk, Chong Zu, Huan Zhao

Abstract: Spin defects in solids offer promising platforms for quantum sensing and memory due to their long coherence times and optical addressability. Here, we integrate a single nitrogen-vacancy (NV) center in diamond with scanning probe microscopy to discover, read out, and spatially map arbitrary spin-based quantum sensors at the nanoscale. Using the boron vacancy ($\mathrm{V}_\mathrm{B}^-$) center in h… ▽ More Spin defects in solids offer promising platforms for quantum sensing and memory due to their long coherence times and optical addressability. Here, we integrate a single nitrogen-vacancy (NV) center in diamond with scanning probe microscopy to discover, read out, and spatially map arbitrary spin-based quantum sensors at the nanoscale. Using the boron vacancy ($\mathrm{V}_\mathrm{B}^-$) center in hexagonal boron nitride$\unicode{x2013}$an emerging two-dimensional spin system$\unicode{x2013}$as a model, we detect its electron spin resonance indirectly via changes in the spin relaxation time ($T_1$) of a nearby NV center, eliminating the need for optical excitation or fluorescence detection of the $\mathrm{V}_\mathrm{B}^-$. Cross-relaxation between NV and $\mathrm{V}_\mathrm{B}^-$ ensembles significantly reduces NV $T_1$, enabling quantitative nanoscale mapping of defect densities beyond the optical diffraction limit and clear resolution of hyperfine splitting in isotopically enriched h$^{10}$B$^{15}$N. Our method demonstrates interactions between 3D and 2D spin sensors, establishing NV centers as versatile probes for characterizing otherwise inaccessible spin defects. △ Less

Submitted 4 March, 2026; v1 submitted 13 April, 2025; originally announced April 2025.

Telecom-wavelength Single-photon Emitters in Multi-layer InSe

Authors: Huan Zhao, Saban Hus, Jinli Chen, Xiaodong Yan, Ben Lawrie, Stephen Jesse, An-Ping Li, Liangbo Liang, Han Htoon

Abstract: The development of robust and efficient single photon emitters (SPEs) at telecom wavelengths is critical for advancements in quantum information science. Two-dimensional (2D) materials have recently emerged as promising sources for SPEs, owing to their high photon extraction efficiency, facile coupling to external fields, and seamless integration into photonic circuits. In this study, we demonstra… ▽ More The development of robust and efficient single photon emitters (SPEs) at telecom wavelengths is critical for advancements in quantum information science. Two-dimensional (2D) materials have recently emerged as promising sources for SPEs, owing to their high photon extraction efficiency, facile coupling to external fields, and seamless integration into photonic circuits. In this study, we demonstrate the creation of SPEs emitting in the 1000 to 1550 nm near-infrared range by coupling 2D indium selenide (InSe) with strain-inducing nanopillar arrays. The emission wavelength exhibits a strong dependence on the number of layers. Hanbury Brown and Twiss experiments conducted at 10 K reveal clear photon antibunching, confirming the single-photon nature of the emissions. Density-functional-theory calculations and scanning-tunneling-microscopy analyses provide insights into the electronic structures and defect states, elucidating the origins of the SPEs. Our findings highlight the potential of multilayer 2D metal monochalcogenides for creating SPEs across a broad spectral range, paving the way for their integration into quantum communication technologies. △ Less

Submitted 22 October, 2024; originally announced October 2024.

Comments: 19 pages, 6 figures

A Versatile Side Entry Laser System for Scanning Transmission Electron Microscopy

Authors: Ondrej Dyck, Olugbenga Olunloyo, Kai Xiao, Benjamin Wolf, Thomas M. Moore, Andrew R. Lupini, Stephen Jesse

Abstract: We present the design and implementation of a side entry laser system designed for an ultra-high vacuum scanning transmission electron microscope. This system uses a versatile probe design enclosed in a vacuum envelope such that parts can be easily aligned, modified, or exchanged without disturbing the vacuum. The system uses a mirror mounted on the sample holder such that the sample can be illumi… ▽ More We present the design and implementation of a side entry laser system designed for an ultra-high vacuum scanning transmission electron microscope. This system uses a versatile probe design enclosed in a vacuum envelope such that parts can be easily aligned, modified, or exchanged without disturbing the vacuum. The system uses a mirror mounted on the sample holder such that the sample can be illuminated without being tilted. Notably the mirror can be removed and replaced with an ablation target and a higher power laser used to ablate material directly onto the sample. We argue that new capabilities hold the potential to transform the electron microscope from an analysis tool towards a more flexible synthesis system, where atomic scale fabrication and atom-by-atom experiments can be performed. △ Less

Submitted 16 October, 2024; v1 submitted 12 July, 2024; originally announced July 2024.

Your Clean Graphene is Still Not Clean

Authors: Ondrej Dyck, Aisha Okmi, Kai Xiao, Sidong Lei, Andrew R. Lupini, Stephen Jesse

Abstract: Efforts aimed at scaling fabrication processes to the level of single atoms, dubbed atom-by-atom fabrication or atomic fabrication, invariably encounter the obstacle of atomic scale cleanliness. When considering atomic fabrication, cleanliness of the base material and purity of the source reservoir from which atomic structures will be built are invariable constraints imposed by laws of physics and… ▽ More Efforts aimed at scaling fabrication processes to the level of single atoms, dubbed atom-by-atom fabrication or atomic fabrication, invariably encounter the obstacle of atomic scale cleanliness. When considering atomic fabrication, cleanliness of the base material and purity of the source reservoir from which atomic structures will be built are invariable constraints imposed by laws of physics and chemistry. As obvious as such statements may be, and regardless of the inevitable consequences for successful atomic fabrication, there is a poignant lack of understanding of the "dirt" (contamination/impurities). Here, we examine hydrocarbon contamination on graphene. Graphene has formed the base substrate for many e-beam-based atomic fabrication studies and many strategies for cleaning graphene have been presented in the literature. One popular method is heating to high temperatures (>500 °C). It is usually inferred that volatile hydrocarbons evaporate into the microscope vacuum system leaving behind pristine graphene. Here, we show through direct image intensity analysis that what appears to be clean graphene can be coated with a thin layer of dynamically diffusing hydrocarbons. This result holds significant implications for approaches to e-beam based atomic fabrication, updates the conceptual model of e-beam induced hydrocarbon deposition, and may extend to hot surfaces generally. △ Less

Submitted 16 October, 2024; v1 submitted 2 July, 2024; originally announced July 2024.

AEcroscoPy: A software-hardware framework empowering microscopy toward automated and autonomous experimentation

Authors: Yongtao Liu, Kevin Roccapriore, Marti Checa, Sai Mani Valleti, Jan-Chi Yang, Stephen Jesse, Rama K. Vasudevan

Abstract: Microscopy, in particular scanning probe and electron microscopy, has been pivotal in improving our understanding of structure-function relationships at the nanoscale and is by now ubiquitous in most research characterization labs and facilities. However, traditional microscopy operations are still limited largely by a human-centric click-and-go paradigm utilizing vendor-provided software, which n… ▽ More Microscopy, in particular scanning probe and electron microscopy, has been pivotal in improving our understanding of structure-function relationships at the nanoscale and is by now ubiquitous in most research characterization labs and facilities. However, traditional microscopy operations are still limited largely by a human-centric click-and-go paradigm utilizing vendor-provided software, which necessarily limits the scope, utility, efficiency, effectiveness, and at times reproducibility of microscopy experiments. Here, we develop a coupled hardware-software platform that consists of a field-programmable gate array (FPGA) device, with LabView-built customized acquisition scripts, along with a software package termed AEcroscoPy (short for Automated Experiments in Microscopy driven by Python) that overcome these limitations and provide the necessary abstractions towards full automation of microscopy platforms. The platform works across multiple vendor devices on scanning probe microscopes and scanning transmission electron microscopes. It enables customized scan trajectories, processing functions that can be triggered locally or remotely on processing servers, user-defined excitation waveforms, standardization of data models, and completely seamless operation through simple Python commands to enable a plethora of microscopy experiments to be performed in a reproducible, automated manner. This platform can be readily coupled with existing machine learning libraries as well as simulations, to provide automated decision-making and active theory-experiment optimization loops to turn microscopes from characterization tools to instruments capable of autonomous model refinement and physics discovery. △ Less

Submitted 15 December, 2023; originally announced December 2023.

Comments: 19 pages, 9 figures

Closed-Loop Electron-Beam-Induced Spectroscopy and Nanofabrication Around Individual Quantum Emitters

Authors: Jawaher Almutlaq, Kyle P. Kelley, Hyeongrak Choi, Linsen Li, Benjamin Lawrie, Ondrej Dyck, Dirk Englund, Stephen Jesse

Abstract: Color centers in diamond play a central role in the development of quantum photonic technologies, and their importance is only expected to grow in the near future. For many quantum applications, high collection efficiency from individual emitters is required, but the refractive index mismatch between diamond and air limits the optimal collection efficiency with conventional diamond device geometri… ▽ More Color centers in diamond play a central role in the development of quantum photonic technologies, and their importance is only expected to grow in the near future. For many quantum applications, high collection efficiency from individual emitters is required, but the refractive index mismatch between diamond and air limits the optimal collection efficiency with conventional diamond device geometries. While different out-coupling methods with near-unity efficiency exist, many have yet to be realized due to current limitations in nanofabrication methods, especially for mechanically hard materials like diamond. Here, we leverage electron-beam-induced etching to modify Sn-implanted diamond quantum microchiplets containing integrated waveguides with width and thickness of 280 nm and 200 nm, respectively. This approach allows for simultaneous high-resolution imaging and modification of the host matrix with an open geometry and direct writing. When coupled with the cathodoluminescence signal generated from the electron-emitter interactions, we can monitor the enhancement of the quantum emitters in real-time with nanoscale spatial resolution. The operando measurement and manipulation of single photon emitters demonstrated here provides a new foundation for the control of emitter-cavity interactions in integrated quantum photonics. △ Less

Submitted 8 December, 2023; originally announced December 2023.

Comments: 7 pages, 4 figures

Direct Fabrication of Atomically Defined Pores in MXenes

Authors: Matthew G. Boebinger, Dundar E. Yilmaz, Ayana Ghosh, Sudhajit Misra, Tyler S. Mathis, Sergei V. Kalinin, Stephen Jesse, Yury Gogotsi, Adri C. T. van Duin, Raymond R. Unocic

Abstract: Controlled fabrication of nanopores in atomically thin two-dimensional material offers the means to create robust membranes needed for ion transport, nanofiltration, and DNA sensing. Techniques for creating nanopores have relied upon either plasma etching or direct irradiation using electrons or ions; however, aberration-corrected scanning transmission electron microscopy (STEM) offers the advanta… ▽ More Controlled fabrication of nanopores in atomically thin two-dimensional material offers the means to create robust membranes needed for ion transport, nanofiltration, and DNA sensing. Techniques for creating nanopores have relied upon either plasma etching or direct irradiation using electrons or ions; however, aberration-corrected scanning transmission electron microscopy (STEM) offers the advantage of combining a highly energetic, sub-angstrom sized electron beam for atomic manipulation along with atomic resolution imaging. Here, we utilize a method for automated nanopore fabrication with real-time atomic visualization to enhance our mechanistic understanding of beam-induced transformations. Additionally, an electron beam simulation technique, Electron-Beam Simulator (E-BeamSim) was developed to observe the atomic movements and interactions resulting from electron beam irradiation. Using the 2D MXene Ti3C2Tx, we explore the influence of temperature on nanopore fabrication by tracking atomic transformation pathways and find that at room temperature, electron beam irradiation induces random displacement of atoms and results in a pileup of titanium atoms at the nanopore edge. This pileup was confirmed and demonstrated in E-BeamSim simulations around the small, milled area in the MXene monolayer. At elevated temperatures, the surface functional groups on MXene are effectively removed, and the mobility of atoms increases, which results in atomic transformations that lead to the selective removal of atoms layer by layer. Through controllable manufacture using e-beam milling fabrication, the production and then characterization of the fabricated defects can be better understood for future work. This work can lead to the development of defect engineering techniques within functionalized MXene layers. △ Less

Submitted 29 November, 2023; originally announced November 2023.

Comments: Experimental and simulations on the electron beam interactions with MXene monolayers to form nanopores as a function of temperature

When the atoms dance: exploring mechanisms of electron-beam induced modifications of materials with machine-learning assisted high temporal resolution electron microscopy

Authors: Matthew G. Boebinger, Ayana Ghosh, Kevin M. Roccapriore, Sudhajit Misra, Kai Xiao, Stephen Jesse, Maxim Ziatdinov, Sergei V. Kalinin, Raymond R. Unocic

Abstract: Directed atomic fabrication using an aberration-corrected scanning transmission electron microscope (STEM) opens new pathways for atomic engineering of functional materials. In this approach, the electron beam is used to actively alter the atomic structure through electron beam induced irradiation processes. One of the impediments that has limited widespread use thus far has been the ability to un… ▽ More Directed atomic fabrication using an aberration-corrected scanning transmission electron microscope (STEM) opens new pathways for atomic engineering of functional materials. In this approach, the electron beam is used to actively alter the atomic structure through electron beam induced irradiation processes. One of the impediments that has limited widespread use thus far has been the ability to understand the fundamental mechanisms of atomic transformation pathways at high spatiotemporal resolution. Here, we develop a workflow for obtaining and analyzing high-speed spiral scan STEM data, up to 100 fps, to track the atomic fabrication process during nanopore milling in monolayer MoS2. An automated feedback-controlled electron beam positioning system combined with deep convolution neural network (DCNN) was used to decipher fast but low signal-to-noise datasets and classify time-resolved atom positions and nature of their evolving atomic defect configurations. Through this automated decoding, the initial atomic disordering and reordering processes leading to nanopore formation was able to be studied across various timescales. Using these experimental workflows a greater degree of speed and information can be extracted from small datasets without compromising spatial resolution. This approach can be adapted to other 2D materials systems to gain further insights into the defect formation necessary to inform future automated fabrication techniques utilizing the STEM electron beam. △ Less

Submitted 12 October, 2023; originally announced October 2023.

Controlling hydrocarbon transport and electron beam induced deposition on single layer graphene: toward atomic scale synthesis in the scanning transmission electron microscope

Authors: Ondrej Dyck, Andrew R. Lupini, Philip D. Rack, Jason Fowlkes, Stephen Jesse

Abstract: Focused electron beam induced deposition (FEBID) is a direct write technique for depositing materials on a support substrate akin to 3D printing with an electron beam (e-beam). Opportunities exist for merging this existing technique with aberration-corrected scanning transmission electron microscopy to achieve molecular- or atomic-level spatial precision. Several demonstrations have been performed… ▽ More Focused electron beam induced deposition (FEBID) is a direct write technique for depositing materials on a support substrate akin to 3D printing with an electron beam (e-beam). Opportunities exist for merging this existing technique with aberration-corrected scanning transmission electron microscopy to achieve molecular- or atomic-level spatial precision. Several demonstrations have been performed using graphene as the support substrate. A common challenge that arises during this process is e-beam-induced hydrocarbon deposition, suggesting greater control over the sample environment is needed. Various strategies exist for cleaning graphene in situ. One of the most effective methods is to rapidly heat to high temperatures, e.g., 600 C or higher. While this can produce large areas of what appears to be atomically clean graphene, mobile hydrocarbons can still be present on the surfaces. Here, we show that these hydrocarbons are primarily limited to surface migration and demonstrate an effective method for interrupting the flow using e-beam deposition to form corralled hydrocarbon regions. This strategy is effective for maintaining atomically clean graphene at high temperatures where hydrocarbon mobility can lead to substantial accumulation of unwanted e-beam deposition. △ Less

Submitted 11 July, 2023; originally announced July 2023.

Doping transition-metal atoms in graphene for atomic-scale tailoring of electronic, magnetic, and quantum topological properties

Authors: Ondrej Dyck, Lizhi Zhang, Mina Yoon, Jacob L. Swett, Dale Hensley, Cheng Zhang, Philip D. Rack, Jason D. Fowlkes, Andrew R. Lupini, Stephen Jesse

Abstract: Atomic-scale fabrication is an outstanding challenge and overarching goal for the nanoscience community. The practical implementation of moving and fixing atoms to a structure is non-trivial considering that one must spatially address the positioning of single atoms, provide a stabilizing scaffold to hold structures in place, and understand the details of their chemical bonding. Free-standing grap… ▽ More Atomic-scale fabrication is an outstanding challenge and overarching goal for the nanoscience community. The practical implementation of moving and fixing atoms to a structure is non-trivial considering that one must spatially address the positioning of single atoms, provide a stabilizing scaffold to hold structures in place, and understand the details of their chemical bonding. Free-standing graphene offers a simplified platform for the development of atomic-scale fabrication and the focused electron beam in a scanning transmission electron microscope can be used to locally induce defects and sculpt the graphene. In this scenario, the graphene forms the stabilizing scaffold and the experimental question is whether a range of dopant atoms can be attached and incorporated into the lattice using a single technique and, from a theoretical perspective, we would like to know which dopants will create technologically interesting properties. Here, we demonstrate that the electron beam can be used to selectively and precisely insert a variety of transition metal atoms into graphene with highly localized control over the doping locations. We use first-principles density functional theory calculations with direct observation of the created structures to reveal the energetics of incorporating metal atoms into graphene and their magnetic, electronic, and quantum topological properties. △ Less

Submitted 11 July, 2023; originally announced July 2023.

Doping of Cr in Graphene Using Electron Beam Manipulation for Functional Defect Engineering

Authors: Ondrej Dyck, Mina Yoon, Lizhi Zhang, Andrew R. Lupini, Jacob L. Swett, Stephen Jesse

Abstract: Chromium atoms in graphene have been proposed to exhibit magnetic moments and spin-selective conducting states depending on the local bonding geometry within the graphene structure, which could lead to interesting applications in spintronics. Despite this interest, there are few direct experimental reports of Cr dopants in graphene even though it is theorized to be stable. Here, we demonstrate the… ▽ More Chromium atoms in graphene have been proposed to exhibit magnetic moments and spin-selective conducting states depending on the local bonding geometry within the graphene structure, which could lead to interesting applications in spintronics. Despite this interest, there are few direct experimental reports of Cr dopants in graphene even though it is theorized to be stable. Here, we demonstrate the introduction of single Cr dopant atoms into the graphene lattice and onto graphene edges through the controlled use of a focused electron beam in a scanning transmission electron microscope. We show local control of doping locations and when coupled with targeted in situ milling during scanning of the e-beam, these strategies demonstrate an important component of the fabrication of tailored nanostructured devices in the electron microscope. The approach is validated with first principles calculations to understand synthesis pathways during fabrication and reveal the energetics and local properties of Cr atoms embedded in graphene; e.g., Cr doping can convert graphene into a magnetic and semiconducting material, which suggests Cr-doped graphene can be used as a building block for potential electronic devices and a means to construct them. △ Less

Submitted 11 July, 2023; originally announced July 2023.

The role of temperature on defect diffusion and nanoscale patterning in graphene

Authors: Ondrej Dyck, Sinchul Yeom, Sarah Dillender, Andrew R. Lupini, Mina Yoon, Stephen Jesse

Abstract: Graphene is of great scientific interest due to a variety of unique properties such as ballistic transport, spin selectivity, the quantum hall effect, and other quantum properties. Nanopatterning and atomic scale modifications of graphene are expected to enable further control over its intrinsic properties, providing ways to tune the electronic properties through geometric and strain effects, intr… ▽ More Graphene is of great scientific interest due to a variety of unique properties such as ballistic transport, spin selectivity, the quantum hall effect, and other quantum properties. Nanopatterning and atomic scale modifications of graphene are expected to enable further control over its intrinsic properties, providing ways to tune the electronic properties through geometric and strain effects, introduce edge states and other local or extended topological defects, and sculpt circuit paths. The focused beam of a scanning transmission electron microscope (STEM) can be used to remove atoms, enabling milling, doping, and deposition. Utilization of a STEM as an atomic scale fabrication platform is increasing; however, a detailed understanding of beam-induced processes and the subsequent cascade of aftereffects is lacking. Here, we examine the electron beam effects on atomically clean graphene at a variety of temperatures ranging from 400 to 1000 C. We find that temperature plays a significant role in the milling rate and moderates competing processes of carbon adatom coalescence, graphene healing, and the diffusion (and recombination) of defects. The results of this work can be applied to a wider range of 2D materials and introduce better understanding of defect evolution in graphite and other bulk layered materials. △ Less

Submitted 11 July, 2023; originally announced July 2023.

A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experiments

Authors: Arpan Biswas, Yongtao Liu, Nicole Creange, Yu-Chen Liu, Stephen Jesse, Jan-Chi Yang, Sergei V. Kalinin, Maxim A. Ziatdinov, Rama K. Vasudevan

Abstract: Optimization of experimental materials synthesis and characterization through active learning methods has been growing over the last decade, with examples ranging from measurements of diffraction on combinatorial alloys at synchrotrons, to searches through chemical space with automated synthesis robots for perovskites. In virtually all cases, the target property of interest for optimization is def… ▽ More Optimization of experimental materials synthesis and characterization through active learning methods has been growing over the last decade, with examples ranging from measurements of diffraction on combinatorial alloys at synchrotrons, to searches through chemical space with automated synthesis robots for perovskites. In virtually all cases, the target property of interest for optimization is defined apriori with limited human feedback during operation. In contrast, here we present the development of a new type of human in the loop experimental workflow, via a Bayesian optimized active recommender system (BOARS), to shape targets on the fly, employing human feedback. We showcase examples of this framework applied to pre-acquired piezoresponse force spectroscopy of a ferroelectric thin film, and then implement this in real time on an atomic force microscope, where the optimization proceeds to find symmetric piezoresponse amplitude hysteresis loops. It is found that such features appear more affected by subsurface defects than the local domain structure. This work shows the utility of human-augmented machine learning approaches for curiosity-driven exploration of systems across experimental domains. The analysis reported here is summarized in Colab Notebook for the purpose of tutorial and application to other data: https://github.com/arpanbiswas52/varTBO △ Less

Submitted 5 April, 2023; originally announced April 2023.

Comments: 7 figures in main text, 3 figures in Supp Material

A platform for in situ synthesis in a STEM

Authors: Ondrej Dyck, Andrew R. Lupini, Stephen Jesse

Abstract: The engineering of quantum materials requires the development of tools able to address various synthesis and characterization challenges. These include the establishment and refinement of growth methods, material manipulation, and defect engineering. Material modification at the atomic level will be a key enabling factor for the engineering of quantum materials where desired phenomena are critical… ▽ More The engineering of quantum materials requires the development of tools able to address various synthesis and characterization challenges. These include the establishment and refinement of growth methods, material manipulation, and defect engineering. Material modification at the atomic level will be a key enabling factor for the engineering of quantum materials where desired phenomena are critically determined by local atomic structures. Successful use of scanning transmission electron microscopes (STEMs) for atomic scale material manipulation has opened the door for a transformed view of what can be accomplished using electron-beam-based strategies. However, serious obstacles exist on the pathway from possibility to practical reality. One such obstacle is the in situ delivery of atomized material in the STEM to the region of interest for further fabrication processes. Here, we present progress on this front with a view toward performing synthesis (deposition and growth) processes in a scanning transmission electron microscope. An in situ thermal deposition platform is presented, tested, and deposition and growth processes are demonstrated. In particular, we show that isolated Sn atoms can be evaporated from a filament and caught on the nearby sample, demonstrating atomized material delivery. This platform, and future variations, are envisioned to facilitate real-time atomic resolution imaging of growth processes and open new pathways toward atomic fabrication. △ Less

Submitted 27 February, 2023; originally announced February 2023.

The Synthescope: A Vision for Combining Synthesis with Atomic Fabrication

Authors: Ondrej Dyck, Andrew R. Lupini, Stephen Jesse

Abstract: The scanning transmission electron microscope, a workhorse instrument in materials characterization, is being transformed into an atomic-scale material manipulation platform. With an eye on the trajectory of recent developments and the obstacles toward progress in this field, we provide a vision for a path toward an expanded set of capabilities and applications. We reconceptualize the microscope a… ▽ More The scanning transmission electron microscope, a workhorse instrument in materials characterization, is being transformed into an atomic-scale material manipulation platform. With an eye on the trajectory of recent developments and the obstacles toward progress in this field, we provide a vision for a path toward an expanded set of capabilities and applications. We reconceptualize the microscope as an instrument for fabrication and synthesis with the capability to image and characterize atomic-scale structural formation as it occurs. Further development and refinement of this approach may have substantial impact on research in microelectronics, quantum information science, and catalysis where precise control over atomic scale structure and chemistry of a few "active sites" can have a dramatic impact on larger scale functionality and where developing a better understanding of atomic scale processes can help point the way to larger scale synthesis approaches. △ Less

Submitted 30 May, 2023; v1 submitted 16 February, 2023; originally announced February 2023.

Direct-Writing Atom-by-Atom

Authors: Ondrej Dyck, Andrew R. Lupini, Stephen Jesse

Abstract: Direct-write processes enable the alteration or deposition of materials in a continuous, directable, sequential fashion. In this work we demonstrate an electron beam direct-write process in an aberration-corrected scanning transmission electron microscope. This process has several fundamental differences from conventional electron beam induced deposition techniques, where the electron beam dissoci… ▽ More Direct-write processes enable the alteration or deposition of materials in a continuous, directable, sequential fashion. In this work we demonstrate an electron beam direct-write process in an aberration-corrected scanning transmission electron microscope. This process has several fundamental differences from conventional electron beam induced deposition techniques, where the electron beam dissociates precursor gases into chemically reactive products that bond to a substrate. Here, we use elemental tin (Sn) as a precursor and employ a different mechanism to facilitate deposition. The atomic-sized electron beam is used to generate chemically reactive point defects at desired locations in a graphene substrate. Temperature control of the sample is used to enable the precursor atoms to migrate across the surface and bond to the defect sites thereby enabling atom-by-atom direct-writing. △ Less

Submitted 20 March, 2023; v1 submitted 6 January, 2023; originally announced January 2023.

Top-down fabrication of atomic patterns in twisted bilayer graphene

Authors: Ondrej Dyck, Sinchul Yeom, Andrew R. Lupini, Jacob L. Swett, Dale Hensley, Mina Yoon, Stephen Jesse

Abstract: Atomic-scale engineering typically involves bottom-up approaches, leveraging parameters such as temperature, partial pressures, and chemical affinity to promote spontaneous arrangement of atoms. These parameters are applied globally, resulting in atomic scale features scattered probabilistically throughout the material. In a top-down approach, different regions of the material are exposed to diffe… ▽ More Atomic-scale engineering typically involves bottom-up approaches, leveraging parameters such as temperature, partial pressures, and chemical affinity to promote spontaneous arrangement of atoms. These parameters are applied globally, resulting in atomic scale features scattered probabilistically throughout the material. In a top-down approach, different regions of the material are exposed to different parameters resulting in structural changes varying on the scale of the resolution. In this work, we combine the application of global and local parameters in an aberration corrected scanning transmission electron microscope (STEM) to demonstrate atomic scale precision patterning of atoms in twisted bilayer graphene. The focused electron beam is used to define attachment points for foreign atoms through the controlled ejection of carbon atoms from the graphene lattice. The sample environment is staged with nearby source materials, such that the sample temperature can induce migration of the source atoms across the sample surface. Under these conditions, the electron-beam (top-down) enables carbon atoms in the graphene to be replaced spontaneously by diffusing adatoms (bottom-up). Using image-based feedback-control, arbitrary patterns of atoms and atom clusters are attached to the twisted bilayer graphene with limited human interaction. The role of substrate temperature on adatom and vacancy diffusion is explored by first-principles simulations. △ Less

Submitted 4 January, 2023; originally announced January 2023.

Direct Imaging of Electron Orbitals with a Scanning Transmission Electron Microscope

Authors: Ondrej Dyck, Jawaher Almutlaq, Jacob L. Swett, Andrew R. Lupini, Dirk Englund, Stephen Jesse

Abstract: Recent studies of secondary electron (SE) emission in scanning transmission electron microscopes suggest that material's properties such as electrical conductivity, connectivity, and work function can be probed with atomic scale resolution using a technique known as secondary electron e-beam-induced current (SEEBIC). Here, we apply the SEEBIC imaging technique to a stacked 2D heterostructure devic… ▽ More Recent studies of secondary electron (SE) emission in scanning transmission electron microscopes suggest that material's properties such as electrical conductivity, connectivity, and work function can be probed with atomic scale resolution using a technique known as secondary electron e-beam-induced current (SEEBIC). Here, we apply the SEEBIC imaging technique to a stacked 2D heterostructure device to reveal the spatially resolved electron orbital ionization cross section of an encapsulated WSe2 layer. We find that the double Se lattice site shows higher emission than the W site, which is at odds with first-principles modelling of ionization of an isolated WSe2 cluster. These results illustrate that atomic level SEEBIC contrast within a single material is possible and that an enhanced understanding of atomic scale SE emission is required to account for the observed contrast. In turn, this suggests that subtle information about interlayer bonding and the effect on electron orbitals can be directly revealed with this technique. △ Less

Submitted 1 December, 2022; originally announced December 2022.

Probing temperature-induced phase transitions at individual ferroelectric domain walls

Authors: Kyle P. Kelley, Sergei V. Kalinin, Eugene Eliseev, Shivaranjan Raghuraman, Stephen Jesse, Peter Maksymovych, Anna N. Morozovska

Abstract: Ferroelectric domain walls have emerged as one of the most fascinating objects in condensed matter physics due to the broad variability of functional behaviors they exhibit. However, the vast majority of domain walls studies have been focused on bias-induced dynamics and transport behaviors. Here, we introduce the scanning probe microscopy approach based on piezoresponse force microscopy (PFM) wit… ▽ More Ferroelectric domain walls have emerged as one of the most fascinating objects in condensed matter physics due to the broad variability of functional behaviors they exhibit. However, the vast majority of domain walls studies have been focused on bias-induced dynamics and transport behaviors. Here, we introduce the scanning probe microscopy approach based on piezoresponse force microscopy (PFM) with a dynamically heated probe, combining local heating and local biasing of the material. This approach is used to explore the thermal polarization dynamics in soft Sn2P2S6 ferroelectrics, and allows for the exploration of phase transitions at individual domain walls. The strong and weak modulation regimes for the thermal PFM are introduced. The future potential applications of heated probe approach for functional SPM measurements including piezoelectric, elastic, microwave, and transport measurements are discussed. △ Less

Submitted 7 July, 2022; originally announced July 2022.

Electron-beam Introduction of Heteroatomic Pt-Si Structures in Graphene

Authors: Ondrej Dyck, Cheng Zhang, Philip D. Rack, Jason D. Fowlkes, Bobby Sumpter, Andrew R. Lupini, Sergei V. Kalinin, Stephen Jesse

Abstract: Electron-beam (e-beam) manipulation of single dopant atoms in an aberration-corrected scanning transmission electron microscope is emerging as a method for directed atomic motion and atom-by-atom assembly. Until now, the dopant species have been limited to atoms closely matched to carbon in terms of ionic radius and capable of strong covalent bonding with carbon atoms in the graphene lattice. In s… ▽ More Electron-beam (e-beam) manipulation of single dopant atoms in an aberration-corrected scanning transmission electron microscope is emerging as a method for directed atomic motion and atom-by-atom assembly. Until now, the dopant species have been limited to atoms closely matched to carbon in terms of ionic radius and capable of strong covalent bonding with carbon atoms in the graphene lattice. In situ dopant insertion into a graphene lattice has thus far been demonstrated only for Si, which is ubiquitously present as a contaminant in this material. Here, we achieve in situ manipulation of Pt atoms and their insertion into the graphene host matrix using the e-beam deposited Pt on graphene as a host system. We further demonstrate a mechanism for stabilization of the Pt atom, enabled through the formation of Si-stabilized Pt heteroatomic clusters attached to the graphene surface. This study provides evidence toward the universality of the e-beam assembly approach, opening a pathway for exploring cluster chemistry through direct assembly. △ Less

Submitted 17 March, 2022; originally announced March 2022.

Comments: 23 pages, 6 figures

Journal ref: Carbon 161, 750-757 (2020)

Mapping Conductance and Switching Behavior of Graphene Devices In Situ

Authors: Ondrej Dyck, Jacob L. Swett, Charalambos Evangeli, Andrew R. Lupini, Jan A. Mol, Stephen Jesse

Abstract: Graphene has been proposed for use in various nanodevice designs, many of which harness emergent quantum properties for device functionality. However, visualization, measurement, and manipulation become non-trivial at nanometer and atomic scales, representing a significant challenge for device fabrication, characterization, and optimization at length scales where quantum effects emerge. Here, we p… ▽ More Graphene has been proposed for use in various nanodevice designs, many of which harness emergent quantum properties for device functionality. However, visualization, measurement, and manipulation become non-trivial at nanometer and atomic scales, representing a significant challenge for device fabrication, characterization, and optimization at length scales where quantum effects emerge. Here, we present proof of principle results at the crossroads between 2D nanoelectronic devices, e-beam-induced modulation, and imaging with secondary electron e-beam induced currents (SEEBIC). We introduce a device platform compatible with scanning transmission electron microscopy investigations. We then show how the SEEBIC imaging technique can be used to visualize conductance and connectivity in single layer graphene nanodevices, even while supported on a thicker substrate (conditions under which conventional imaging fails). Finally, we show that the SEEBIC imaging technique can detect subtle differences in charge transport through time in non-ohmic graphene nanoconstrictions indicating the potential to reveal dynamic electronic processes. △ Less

Submitted 17 March, 2022; originally announced March 2022.

Comments: 25 pages, 15 figures

Journal ref: Small Methods 2021, 2101245

Contrast mechanisms in secondary electron e-beam induced current (SEEBIC) imaging

Authors: Ondrej Dyck, Jacob L. Swett, Charalambos Evangeli, Andrew R. Lupini, Jan Mol, Stephen Jesse

Abstract: Over the last few years, a new mode for imaging in the scanning transmission electron microscope (STEM) has gained attention as it permits the direct visualization of sample conductivity and electrical connectivity. When the electron beam (e-beam) is focused on the sample in the STEM, secondary electrons (SEs) are generated. If the sample is conductive and electrically connected to an amplifier, t… ▽ More Over the last few years, a new mode for imaging in the scanning transmission electron microscope (STEM) has gained attention as it permits the direct visualization of sample conductivity and electrical connectivity. When the electron beam (e-beam) is focused on the sample in the STEM, secondary electrons (SEs) are generated. If the sample is conductive and electrically connected to an amplifier, the SE current can be measured as a function of the e-beam position. This scenario is similar to the better-known scanning electron microscopy (SEM)-based technique, electron beam induced current (EBIC) imaging except the signal in STEM is generated by the emission of SEs, hence the name SEEBIC, and in this case the current flows in the opposite direction. Here, we provide a brief review of recent work in this area, examine the various contrast generation mechanisms associated with SEEBIC, and illustrate its use for the characterization of graphene nanoribbon devices. △ Less

Submitted 15 March, 2022; originally announced March 2022.

Comments: 49 pages, 13 figures

Van der Waals Epitaxy on Freestanding Monolayer Graphene Membrane by MBE

Authors: Jason Lapano, Ondrej Dyck, Andrew Lupini, Wonhee Ko, Haoxiang Li, Hu Miao, Ho Nyung Lee, An-Ping Li, Matthew Brahlek, Stephen Jesse, Robert G. Moore

Abstract: Research on two-dimensional materials has expanded over the past two decades to become a central theme in condensed matter research today. Significant advances have been made in the synthesis and subsequent reassembly of these materials using mechanical methods into a vast array of hybrid structures with novel properties and ever-increasing potential applications. The key hurdles in realizing this… ▽ More Research on two-dimensional materials has expanded over the past two decades to become a central theme in condensed matter research today. Significant advances have been made in the synthesis and subsequent reassembly of these materials using mechanical methods into a vast array of hybrid structures with novel properties and ever-increasing potential applications. The key hurdles in realizing this potential are the challenges in controlling the atomic structure of these layered hybrid materials and the difficulties in harnessing their unique functionality with existing semiconductor nanofabrication techniques. Here we report on high-quality van der Waals epitaxial growth and characterization of a layered topological insulator on freestanding monolayer graphene transferred to different mechanical supports. This templated synthesis approach enables direct interrogation of interfacial atomic structure of these as-grown hybrid structures and opens a route towards creating device structures with more traditional semiconductor nanofabrication techniques. △ Less

Submitted 29 March, 2021; originally announced March 2021.

Comments: 13 pages, 4 figures

Automated and Autonomous Experiment in Electron and Scanning Probe Microscopy

Authors: Sergei V. Kalinin, Maxim A. Ziatdinov, Jacob Hinkle, Stephen Jesse, Ayana Ghosh, Kyle P. Kelley, Andrew R. Lupini, Bobby G. Sumpter, Rama K. Vasudevan

Abstract: Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with domain applications ranging from theory and materials prediction to high-throughput data analysis. In parallel, the recent successes in applying ML/AI methods for autonomous systems from robotics through self-driving cars to organic and inorganic synthesis are generating enthus… ▽ More Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with domain applications ranging from theory and materials prediction to high-throughput data analysis. In parallel, the recent successes in applying ML/AI methods for autonomous systems from robotics through self-driving cars to organic and inorganic synthesis are generating enthusiasm for the potential of these techniques to enable automated and autonomous experiment (AE) in imaging. Here, we aim to analyze the major pathways towards AE in imaging methods with sequential image formation mechanisms, focusing on scanning probe microscopy (SPM) and (scanning) transmission electron microscopy ((S)TEM). We argue that automated experiments should necessarily be discussed in a broader context of the general domain knowledge that both informs the experiment and is increased as the result of the experiment. As such, this analysis should explore the human and ML/AI roles prior to and during the experiment, and consider the latencies, biases, and knowledge priors of the decision-making process. Similarly, such discussion should include the limitations of the existing imaging systems, including intrinsic latencies, non-idealities and drifts comprising both correctable and stochastic components. We further pose that the role of the AE in microscopy is not the exclusion of human operators (as is the case for autonomous driving), but rather automation of routine operations such as microscope tuning, etc., prior to the experiment, and conversion of low latency decision making processes on the time scale spanning from image acquisition to human-level high-order experiment planning. △ Less

Submitted 22 March, 2021; originally announced March 2021.

Autonomous Experiments in Scanning Probe Microscopy and Spectroscopy: Choosing Where to Explore Polarization Dynamics in Ferroelectrics

Authors: Rama K. Vasudevan, Kyle Kelley, Jacob Hinkle, Hiroshi Funakubo, Stephen Jesse, Sergei V. Kalinin, Maxim Ziatdinov

Abstract: Polarization dynamics in ferroelectric materials are explored via the automated experiment in Piezoresponse Force Spectroscopy. A Bayesian Optimization framework for imaging is developed and its performance for a variety of acquisition and pathfinding functions is explored using previously acquired data. The optimized algorithm is then deployed on an operational scanning probe microscope (SPM) for… ▽ More Polarization dynamics in ferroelectric materials are explored via the automated experiment in Piezoresponse Force Spectroscopy. A Bayesian Optimization framework for imaging is developed and its performance for a variety of acquisition and pathfinding functions is explored using previously acquired data. The optimized algorithm is then deployed on an operational scanning probe microscope (SPM) for finding areas of large electromechanical response in a thin film of PbTiO3, with metrics showing gains of ~3x in the sampling efficiency. This approach opens the pathway to perform more complex spectroscopies in SPM that were previously not possible due to time constraints and sample stability, tip wear, and/or stochastic sample damage that occurs at specific, a priori unknown spatial positions. Potential improvements to the framework to enable the incorporation of more prior information and improve efficiency further are discussed. △ Less

Submitted 22 June, 2021; v1 submitted 25 November, 2020; originally announced November 2020.

Exploring order parameters and dynamic processes in disordered systems via variational autoencoders

Authors: Sergei V. Kalinin, Ondrej Dyck, Stephen Jesse, Maxim Ziatdinov

Abstract: We suggest and implement an approach for the bottom-up description of systems undergoing large-scale structural changes and chemical transformations from dynamic atomically resolved imaging data, where only partial or uncertain data on atomic positions are available. This approach is predicated on the synergy of two concepts, the parsimony of physical descriptors and general rotational invariance… ▽ More We suggest and implement an approach for the bottom-up description of systems undergoing large-scale structural changes and chemical transformations from dynamic atomically resolved imaging data, where only partial or uncertain data on atomic positions are available. This approach is predicated on the synergy of two concepts, the parsimony of physical descriptors and general rotational invariance of non-crystalline solids, and is implemented using a rotationally-invariant extension of the variational autoencoder applied to semantically segmented atom-resolved data seeking the most effective reduced representation for the system that still contains the maximum amount of original information. This approach allowed us to explore the dynamic evolution of electron beam-induced processes in a silicon-doped graphene system, but it can be also applied for a much broader range of atomic-scale and mesoscopic phenomena to introduce the bottom-up order parameters and explore their dynamics with time and in response to external stimuli. △ Less

Submitted 13 April, 2021; v1 submitted 18 June, 2020; originally announced June 2020.

Comments: The revised version

Journal ref: Science Advances 7, eabd5084 (2021)

Fast Scanning Probe Microscopy via Machine Learning: Non-rectangular scans with compressed sensing and Gaussian process optimization

Authors: Kyle P. Kelley, Maxim Ziatdinov, Liam Collins, Michael A. Susner, Rama K. Vasudevan, Nina Balke, Sergei V. Kalinin, Stephen Jesse

Abstract: Fast scanning probe microscopy enabled via machine learning allows for a broad range of nanoscale, temporally resolved physics to be uncovered. However, such examples for functional imaging are few in number. Here, using piezoresponse force microscopy (PFM) as a model application, we demonstrate a factor of 5.8 improvement in imaging rate using a combination of sparse spiral scanning with compress… ▽ More Fast scanning probe microscopy enabled via machine learning allows for a broad range of nanoscale, temporally resolved physics to be uncovered. However, such examples for functional imaging are few in number. Here, using piezoresponse force microscopy (PFM) as a model application, we demonstrate a factor of 5.8 improvement in imaging rate using a combination of sparse spiral scanning with compressive sensing and Gaussian processing reconstruction. It is found that even extremely sparse scans offer strong reconstructions with less than 6 % error for Gaussian processing reconstructions. Further, we analyze the error associated with each reconstructive technique per reconstruction iteration finding the error is similar past approximately 15 iterations, while at initial iterations Gaussian processing outperforms compressive sensing. This study highlights the capabilities of reconstruction techniques when applied to sparse data, particularly sparse spiral PFM scans, with broad applications in scanning probe and electron microscopies. △ Less

Submitted 23 April, 2020; originally announced April 2020.

To switch or not to switch -- a machine learning approach for ferroelectricity

Authors: Sabine M. Neumayer, Stephen Jesse, Gabriel Velarde, Andrei L. Kholkin, Ivan Kravchenko, Lane W. Martin, Nina Balke, Peter Maksymovych

Abstract: With the advent of increasingly elaborate experimental techniques in physics, chemistry and materials sciences, measured data are becoming bigger and more complex. The observables are typically a function of several stimuli resulting in multidimensional data sets spanning a range of experimental parameters. As an example, a common approach to study ferroelectric switching is to observe effects of… ▽ More With the advent of increasingly elaborate experimental techniques in physics, chemistry and materials sciences, measured data are becoming bigger and more complex. The observables are typically a function of several stimuli resulting in multidimensional data sets spanning a range of experimental parameters. As an example, a common approach to study ferroelectric switching is to observe effects of applied electric field, but switching can also be enacted by pressure and is influenced by strain fields, material composition, temperature, time, etc. Moreover, the parameters are usually interdependent, so that their decoupling toward univariate measurements or analysis may not be straightforward. On the other hand, both explicit and hidden parameters provide an opportunity to gain deeper insight into the measured properties, provided there exists a well-defined path to capture and analyze such data. Here, we introduce a new, two-dimensional approach to represent hysteretic response of a material system to applied electric field. Utilizing ferroelectric polarization as a model hysteretic property, we demonstrate how explicit consideration of electromechanical response to two rather than one control voltages enables significantly more transparent and robust interpretation of observed hysteresis, such as differentiating between charge trapping and ferroelectricity. Furthermore, we demonstrate how the new data representation readily fits into a variety of machinelearning methodologies, from unsupervised classification of the origins of hysteretic response via linear clustering algorithms to neural-network-based inference of the sample temperature based on the specific morphology of hysteresis. △ Less

Submitted 17 April, 2020; originally announced April 2020.

Reconstruction of effective potential from statistical analysis of dynamic trajectories

Authors: Ali Yousefzadi Nobakht, Ondrej Dyck, David B. Lingerfelt, Feng Bao, Maxim Ziatdinov, Artem Maksov, Bobby G. Sumpter, Richard Archibald, Stephen Jesse, Sergei V. Kalinin, Kody J. H. Law

Abstract: The broad incorporation of microscopic methods is yielding a wealth of information on atomic and mesoscale dynamics of individual atoms, molecules, and particles on surfaces and in open volumes. Analysis of such data necessitates statistical frameworks to convert observed dynamic behaviors to effective properties of materials. Here we develop a method for stochastic reconstruction of effective act… ▽ More The broad incorporation of microscopic methods is yielding a wealth of information on atomic and mesoscale dynamics of individual atoms, molecules, and particles on surfaces and in open volumes. Analysis of such data necessitates statistical frameworks to convert observed dynamic behaviors to effective properties of materials. Here we develop a method for stochastic reconstruction of effective acting potentials from observed trajectories. Using the Silicon vacancy defect in graphene as a model, we develop a statistical framework to reconstruct the free energy landscape from calculated atomic displacements. △ Less

Submitted 27 February, 2020; originally announced February 2020.

Comments: 12 pages, 5 figures. This manuscript is a part of update to previous work (arXiv:1804.03729v1) authors found some of the analysis in the previous work to be not accurate and this manuscript is a partial update to that work

Deep learning of interface structures from the 4D STEM data: cation intermixing vs. roughening

Authors: Mark P. Oxley, Junqi Yin, Nikolay Borodinov, Suhas Somnath, Maxim Ziatdinov, Andrew R. Lupini, Stephen Jesse, Rama K. Vasudevan, Sergei V. Kalinin

Abstract: Interface structures in complex oxides remain one of the active areas of condensed matter physics research, largely enabled by recent advances in scanning transmission electron microscopy (STEM). Yet the nature of the STEM contrast in which the structure is projected along the given direction precludes separation of possible structural models. Here, we utilize deep convolutional neural networks (D… ▽ More Interface structures in complex oxides remain one of the active areas of condensed matter physics research, largely enabled by recent advances in scanning transmission electron microscopy (STEM). Yet the nature of the STEM contrast in which the structure is projected along the given direction precludes separation of possible structural models. Here, we utilize deep convolutional neural networks (DCNN) trained on simulated 4D scanning transmission electron microscopy (STEM) datasets to predict structural descriptors of interfaces. We focus on the widely studied interface between LaAlO3 and SrTiO3, using dynamical diffraction theory and leveraging high performance computing to simulate thousands of possible 4D STEM datasets to train the DCNN to learn properties of the underlying structures on which the simulations are based. We validate the DCNN on simulated data and show that it is possible (with >95% accuracy) to identify a physically rough from a chemically diffuse interface and achieve 85% accuracy in determination of buried step positions within the interface. The method shown here is general and can be applied for any inverse imaging problem where forward models are present. △ Less

Submitted 20 February, 2020; originally announced February 2020.

Comments: 18 pages, 4 figures

Bayesian inference in band excitation Scanning Probe Microscopy for optimal dynamic model selection in imaging

Authors: Rama K. Vasudevan, Kyle P. Kelley, Eugene Eliseev, Stephen Jesse, Hiroshi Funakubo, Anna Morozovska, Sergei V. Kalinin

Abstract: The universal tendency in scanning probe microscopy (SPM) over the last two decades is to transition from simple 2D imaging to complex detection and spectroscopic imaging modes. The emergence of complex SPM engines brings forth the challenge of reliable data interpretation, i.e. conversion from detected signal to descriptors specific to tip-surface interactions and subsequently to materials proper… ▽ More The universal tendency in scanning probe microscopy (SPM) over the last two decades is to transition from simple 2D imaging to complex detection and spectroscopic imaging modes. The emergence of complex SPM engines brings forth the challenge of reliable data interpretation, i.e. conversion from detected signal to descriptors specific to tip-surface interactions and subsequently to materials properties. Here, we implemented a Bayesian inference approach for the analysis of the image formation mechanisms in band excitation (BE) SPM. Compared to the point estimates in classical functional fit approaches, Bayesian inference allows for the incorporation of extant knowledge of materials and probe behavior in the form of corresponding prior distribution and return the information on the material functionality in the form of readily interpretable posterior distributions. We note that in application of Bayesian methods, special care should be made for proper setting on the problem as model selection vs. establishing practical parameter equivalence. We further explore the non-linear mechanical behaviors at topological defects in a classical ferroelectric material, PbTiO3. We observe the non-trivial evolution of Duffing resonance frequency and the nonlinearity of the sample surface, suggesting the presence of the hidden elements of domain structure. These observations suggest that the spectrum of anomalous behaviors at the ferroelectric domain walls can be significantly broader than previously believed and can extend to non-conventional mechanical properties in addition to static and microwave conductance. △ Less

Submitted 19 February, 2020; originally announced February 2020.

Comments: Supplementary materials is located at the end of the manuscript

Reconstruction of the interatomic forces from dynamic Scanning Transmission Electron Microscopy data

Authors: M. Chakraborty, M. Ziatdinov, O. Dyck, S. Jesse, A. D. White, S. V. Kalinin

Abstract: We explore the possibility for the reconstruction of the generative physical models describing interactions between atomic units in solids from observational electron microscopy data. Here, scanning transmission electron microscopy (STEM) is used to observe the dynamic motion of Si atoms at the edge of monolayer graphene under continuous electron beam illumination. The resulting time-lapsed STEM i… ▽ More We explore the possibility for the reconstruction of the generative physical models describing interactions between atomic units in solids from observational electron microscopy data. Here, scanning transmission electron microscopy (STEM) is used to observe the dynamic motion of Si atoms at the edge of monolayer graphene under continuous electron beam illumination. The resulting time-lapsed STEM images represent the snapshots of observed chemical states of the system. We use two approaches: potential of mean force (PMF) calculation using a radial distribution function (RDF) and a direct fitting of the graphene-Si interatomic pair-wise potentials with force matching, to reconstruct the force fields in the materials. These studies lay the foundation for quantitative analysis of materials energetics from STEM data through the sampling of the metastable states in the chemical space of the system. △ Less

Submitted 30 January, 2021; v1 submitted 13 February, 2020; originally announced February 2020.

Comments: Update with the accepted version

Journal ref: Journal of Applied Physics 127, 224301 (2020)

Super-resolution and signal separation in contact Kelvin probe force microscopy of electrochemically active ferroelectric materials

Authors: Maxim Ziatdinov, Dohyung Kim, Sabine Neumayer, Liam Collins, Mahshid Ahmadi, Rama K. Vasudevan, Stephen Jesse, Myung Hyun Ann, Jong H. Kim, Sergei V. Kalinin

Abstract: Imaging mechanisms in contact Kelvin Probe Force Microscopy (cKPFM) are explored via information theory-based methods. Gaussian Processes are used to achieve super-resolution in the cKPFM signal, effectively extrapolating across the spatial and parameter space. Tensor matrix factorization is applied to reduce the multidimensional signal to the tensor convolution of the scalar functions that show c… ▽ More Imaging mechanisms in contact Kelvin Probe Force Microscopy (cKPFM) are explored via information theory-based methods. Gaussian Processes are used to achieve super-resolution in the cKPFM signal, effectively extrapolating across the spatial and parameter space. Tensor matrix factorization is applied to reduce the multidimensional signal to the tensor convolution of the scalar functions that show clear trending behavior with the imaging parameters. These methods establish a workflow for the analysis of the multidimensional data sets, that can then be related to the relevant physical mechanisms. We also provide an interactive Google Colab notebook (http://bit.ly/39kMtuR) that goes through all the analysis discussed in the paper. △ Less

Submitted 9 August, 2020; v1 submitted 10 February, 2020; originally announced February 2020.

Comments: Update with accepted version

Journal ref: Journal of Applied Physics 128, 055101 (2020)

Dynamic manipulation in piezoresponse force microscopy: creating non-equilibrium phases with large electromechanical response

Authors: Kyle P. Kelley, Yao Ren, Anna N. Morozovska, Eugene A. Eliseev, Yoshitaka Ehara, Hiroshi Funakubo, Thierry Giamarchi, Nina Balke, Rama K. Vasudevan, Ye Cao, Stephen Jesse, Sergei V. Kalinin

Abstract: Domains walls and topological defects in ferroelectric materials have emerged as a powerful new paradigm for functional electronic devices including memory and logic. Similarly, wall interactions and dynamics underpin a broad range of mesoscale phenomena ranging from giant electromechanical responses to memory effects. Exploring the functionalities of individual domain walls, their interactions, a… ▽ More Domains walls and topological defects in ferroelectric materials have emerged as a powerful new paradigm for functional electronic devices including memory and logic. Similarly, wall interactions and dynamics underpin a broad range of mesoscale phenomena ranging from giant electromechanical responses to memory effects. Exploring the functionalities of individual domain walls, their interactions, and controlled modifications of the domain structures is crucial for applications and fundamental physical studies. However, the dynamic nature of these features severely limits studies of their local physics since application of local biases or pressures in piezoresponse force microscopy induce wall displacement as a primary response. Here, we introduce a fundamentally new approach for the control and modification of domain structures based on automated experimentation whereby real space image-based feedback is used to control the tip bias during ferroelectric switching, allowing for modification routes conditioned on domain states under the tip. This automated experiment approach is demonstrated for the exploration of domain wall dynamics and creation of metastable phases with large electromechanical response. △ Less

Submitted 10 January, 2020; originally announced January 2020.

Direct matter disassembly via electron beam control: Electron-beam-mediated catalytic etching of graphene by nanoparticles

Authors: Ondrej Dyck, David Lingerfelt, Songkil Kim, Stephen Jesse, Sergei V. Kalinin

Abstract: We report electron-beam activated motion of a catalytic nanoparticle along a graphene step edge and associated etching of the edge. This approach enables beam-controlled etching of matter through activated electrocatalytic processes. The applications of electron-beam control as a paradigm for molecular-scale robotics are discussed. We report electron-beam activated motion of a catalytic nanoparticle along a graphene step edge and associated etching of the edge. This approach enables beam-controlled etching of matter through activated electrocatalytic processes. The applications of electron-beam control as a paradigm for molecular-scale robotics are discussed. △ Less

Submitted 16 December, 2019; originally announced December 2019.

Piezoresponse phase as variable in electromechanical characterization

Authors: Sabine M. Neumayer, Sahar Saremi, Lane W. Martin, Liam Collins, Alexander Tselev, Stephen Jesse, Sergei V. Kalinin, Nina Balke

Abstract: Piezoresponse force microscopy (PFM) is a powerful characterization technique to readily image and manipulate ferroelectrics domains. PFM gives insight into the strength of local piezoelectric coupling as well as polarization direction through PFM amplitude and phase, respectively. Converting measured arbitrary units to physical material parameters, however, remains a challenge. While much effort… ▽ More Piezoresponse force microscopy (PFM) is a powerful characterization technique to readily image and manipulate ferroelectrics domains. PFM gives insight into the strength of local piezoelectric coupling as well as polarization direction through PFM amplitude and phase, respectively. Converting measured arbitrary units to physical material parameters, however, remains a challenge. While much effort has been spent on quantifying the PFM amplitude signal, little attention has been given to the PFM phase and it is often arbitrarily adjusted to fit expectations or processed as recorded. This is problematic when investigating materials with unknown or potentially negative sign of the probed effective electrostrictive coefficient or strong frequency dispersion of electromechanical responses since assumptions about the phase cannot be reliably made. The PFM phase can, however, provide important information on the polarization orientation and the sign of the electrostrictive coefficient. Most notably, the orientation of the PFM hysteresis loop is determined by the PFM phase. Moreover, when presenting PFM data as a combined signal, the resulting response can be artificially lowered or asymmetric if the phase data has not been correctly processed. Here, we demonstrate a path to identify the phase offset required to extract correct meaning from PFM phase data. We explore different sources of phase offsets including the experimental setup, instrumental contributions, and data analysis. We discuss the physical working principles of PFM and develop a strategy to extract physical meaning from the PFM phase. The proposed procedures are verified on two materials with positive and negative piezoelectric coefficients. △ Less

Submitted 6 December, 2019; originally announced December 2019.

Comments: 16 pages, 6 figures

Imaging Mechanism for Hyperspectral Scanning Probe Microscopy via Gaussian Process Modelling

Authors: Maxim Ziatdinov, Dohyung Kim, Sabine Neumayer, Rama K. Vasudevan, Liam Collins, Stephen Jesse, Mahshid Ahmadi, Sergei V. Kalinin

Abstract: We investigate the ability to reconstruct and derive spatial structure from sparsely sampled 3D piezoresponse force microcopy data, captured using the band-excitation (BE) technique, via Gaussian Process (GP) methods. Even for weakly informative priors, GP methods allow unambiguous determination of the characteristic length scales of the imaging process both in spatial and frequency domains. We fu… ▽ More We investigate the ability to reconstruct and derive spatial structure from sparsely sampled 3D piezoresponse force microcopy data, captured using the band-excitation (BE) technique, via Gaussian Process (GP) methods. Even for weakly informative priors, GP methods allow unambiguous determination of the characteristic length scales of the imaging process both in spatial and frequency domains. We further show that BE data set tends to be oversampled, with ~30% of the original data set sufficient for high-quality reconstruction, potentially enabling the faster BE imaging. Finally, we discuss how the GP can be used for automated experimentation in SPM, by combining GP regression with non-rectangular scans. The full code for GP regression applied to hyperspectral data is available at https://git.io/JePGr. △ Less

Submitted 26 November, 2019; originally announced November 2019.

Structure retrieval from 4D-STEM: statistical analysis of potential pitfalls in high-dimensional data

Authors: Xin Li, Ondrej Dyck, Stephen Jesse, Andrew R. Lupini, Sergei V. Kalinin, Mark P. Oxley

Abstract: Four-dimensional scanning transmission electron microscopy (4D-STEM) is one of the most rapidly growing modes of electron microscopy imaging. The advent of fast pixelated cameras and the associated data infrastructure have greatly accelerated this process. Yet conversion of the 4D datasets into physically meaningful structure images in real-space remains an open issue. In this work, we demonstrate… ▽ More Four-dimensional scanning transmission electron microscopy (4D-STEM) is one of the most rapidly growing modes of electron microscopy imaging. The advent of fast pixelated cameras and the associated data infrastructure have greatly accelerated this process. Yet conversion of the 4D datasets into physically meaningful structure images in real-space remains an open issue. In this work, we demonstrate that, it is possible to systematically create filters that will affect the apparent resolution or even qualitative features of the real-space structure image, reconstructing artificially generated patterns. As initial efforts, we explore statistical model selection algorithms, aiming for robustness and reliability of estimated filters. This statistical model selection analysis demonstrates the need for regularization and cross-validation of inversion methods to robustly recover structure from high-dimensional diffraction datasets. △ Less

Submitted 26 August, 2019; v1 submitted 1 August, 2019; originally announced August 2019.

Journal ref: Phys. Rev. E 100, 023308, 2019

USID and Pycroscopy -- Open frameworks for storing and analyzing spectroscopic and imaging data

Authors: Suhas Somnath, Chris R. Smith, Nouamane Laanait, Rama K. Vasudevan, Anton Ievlev, Alex Belianinov, Andrew R. Lupini, Mallikarjun Shankar, Sergei V. Kalinin, Stephen Jesse

Abstract: Materials science is undergoing profound changes due to advances in characterization instrumentation that have resulted in an explosion of data in terms of volume, velocity, variety and complexity. Harnessing these data for scientific research requires an evolution of the associated computing and data infrastructure, bridging scientific instrumentation with super- and cloud- computing. Here, we de… ▽ More Materials science is undergoing profound changes due to advances in characterization instrumentation that have resulted in an explosion of data in terms of volume, velocity, variety and complexity. Harnessing these data for scientific research requires an evolution of the associated computing and data infrastructure, bridging scientific instrumentation with super- and cloud- computing. Here, we describe Universal Spectroscopy and Imaging Data (USID), a data model capable of representing data from most common instruments, modalities, dimensionalities, and sizes. We pair this schema with the hierarchical data file format (HDF5) to maximize compatibility, exchangeability, traceability, and reproducibility. We discuss a family of community-driven, open-source, and free python software packages for storing, processing and visualizing data. The first is pyUSID which provides the tools to read and write USID HDF5 files in addition to a scalable framework for parallelizing data analysis. The second is Pycroscopy, which provides algorithms for scientific analysis of nanoscale imaging and spectroscopy modalities and is built on top of pyUSID and USID. The instrument-agnostic nature of USID facilitates the development of analysis code independent of instrumentation and task in Pycroscopy which in turn can bring scientific communities together and break down barriers in the age of open-science. The interested reader is encouraged to be a part of this ongoing community-driven effort to collectively accelerate materials research and discovery through the realms of big data. △ Less

Submitted 27 March, 2019; v1 submitted 22 March, 2019; originally announced March 2019.

Atomic mechanisms for the Si atom dynamics in graphene: chemical transformations at the edge and in the bulk

Authors: Maxim Ziatdinov, Ondrej Dyck, Stephen Jesse, Sergei V. Kalinin

Abstract: Recent advances in scanning transmission electron microscopy (STEM) allow to observe solid-state transformations and reactions in materials induced by thermal stimulus or electron beam on the atomic level. However, despite the rate at which large volumes of data can be generated (sometimes in the gigabyte to terabyte range per single experiment), approaches for the extraction of material-specific… ▽ More Recent advances in scanning transmission electron microscopy (STEM) allow to observe solid-state transformations and reactions in materials induced by thermal stimulus or electron beam on the atomic level. However, despite the rate at which large volumes of data can be generated (sometimes in the gigabyte to terabyte range per single experiment), approaches for the extraction of material-specific knowledge on the kinetics and thermodynamics of these processes are still lacking. One of the critical issues lies in being able to map the evolution of various atomic structures and determine the associated transition probabilities directly from raw experimental data characterized by high levels of noise and missing structural elements. Here, we demonstrate an approach based on the combination of multiple machine learning techniques to study the dynamic behavior of e-beam irradiated Si atoms in the bulk and at the edges of single-layer graphene in STEM experiments. First, a deep learning network is used to convert experimental STEM movies into coordinates of individual Si and carbon atoms. Then, a Gaussian mixture model is further used to establish the elementary atomic configurations of the Si atoms, defining the bonding geometries and chemical species and accounting for the discrete rotational symmetry of the host lattice. Finally, the frequencies and Markov transition probabilities between these states are determined. This analysis enables insight into the thermodynamics of defect populations and chemical reaction networks from the atomically resolved STEM data. Here, we observe a clear tendency for the formation of a 1D Si crystal along zigzag direction of graphene edges and for the Si impurity coupling to topological defects in bulk graphene. △ Less

Submitted 27 January, 2019; originally announced January 2019.

Manifold Learning of Four-dimensional Scanning Transmission Electron Microscopy

Authors: Xin Li, Ondrej E. Dyck, Mark P. Oxley, Andrew R. Lupini, Leland McInnes, John Healy, Stephen Jesse, Sergei V. Kalinin

Abstract: Four-dimensional scanning transmission electron microscopy (4D-STEM) of local atomic diffraction patterns is emerging as a powerful technique for probing intricate details of atomic structure and atomic electric fields. However, efficient processing and interpretation of large volumes of data remain challenging, especially for two-dimensional or light materials because the diffraction signal recor… ▽ More Four-dimensional scanning transmission electron microscopy (4D-STEM) of local atomic diffraction patterns is emerging as a powerful technique for probing intricate details of atomic structure and atomic electric fields. However, efficient processing and interpretation of large volumes of data remain challenging, especially for two-dimensional or light materials because the diffraction signal recorded on the pixelated arrays is weak. Here we employ data-driven manifold leaning approaches for straightforward visualization and exploration analysis of the 4D-STEM datasets, distilling real-space neighboring effects on atomically resolved deflection patterns from single-layer graphene, with single dopant atoms, as recorded on a pixelated detector. These extracted patterns relate to both individual atom sites and sublattice structures, effectively discriminating single dopant anomalies via multi-mode views. We believe manifold learning analysis will accelerate physics discoveries coupled between data-rich imaging mechanisms and materials such as ferroelectric, topological spin and van der Waals heterostructures. △ Less

Submitted 13 January, 2019; v1 submitted 18 October, 2018; originally announced November 2018.

Journal ref: npj Computational Materials volume 5, Article number: 5 (2019)