Showing 1–46 of 46 results for author: Yoo, S

Inverse mask design for interference lithography using automatic differentiable wave propagation

Authors: Chuntian Cao, Jangwoon Sung, Jack Griffiths, Yuan Gao, Xi Yu, Paul Baity, Nikhil Tiwale, Zhitian Shi, Juhong Ahn, Shinjae Yoo, Yong S. Chu, Chang-Yong Nam

Abstract: Interference lithography (IL) is powerful for fabricating high-resolution periodic nanostructures, but designing masks to produce non-periodic patterns remains challenging. We introduce a gradient-based optimization framework for binary IL mask design using automatic differentiation. The forward model is implemented using the differentiable angular spectrum method (ASM). The inverse mask design is… ▽ More Interference lithography (IL) is powerful for fabricating high-resolution periodic nanostructures, but designing masks to produce non-periodic patterns remains challenging. We introduce a gradient-based optimization framework for binary IL mask design using automatic differentiation. The forward model is implemented using the differentiable angular spectrum method (ASM). The inverse mask design is formulated as an optimization problem, where the mask logits are updated through backpropagation of the loss between the simulated field amplitude and the target pattern. We optimize a mask that reproduces a target pattern with only 0.1% isolated pixel-level defects, resolving features at half the mask pixel pitch. To scale mask optimization, we employ the shifted ASM, which partitions the mask into patches that are propagated independently and summed at the image plane. For a 3.84 mm$\times$3.84 mm mask, shifted ASM with 16 patches reduces peak GPU memory by 3.8$\times$ at only 1.3$\times$ runtime cost relative to standard ASM. With gradient checkpointing, peak memory is reduced by 7.4$\times$ at 2$\times$ runtime. Distributing across multiple GPUs further accelerates the optimization. This work establishes a physics-informed, machine learning-driven approach for IL mask design, moving a step further towards complex, non-periodic patterns. The source code is available at https://github.com/chuntian236/holography-optimization.git . △ Less

Submitted 5 August, 2026; originally announced August 2026.

Comments: 11 pages, 4 figures. To be published in Proceedings of SPIE Optics + Photonics 2026, Optical Engineering + Applications

Silicon Photonics-based Heterodyne Interferometric Imager for free-space imaging

Authors: Humphry Chen, Mingye Fu, Shun-Hung Lee, Shelbe Timothy, Lawrence Shing, Gopal Vasudevan, Tony Kowalczyk, Neal Hurlburt, Sung-Joo Ben Yoo

Abstract: This paper reports on the design, fabrication, and demonstration of a silicon photonics based heterodyne interferometric imaging system. The photonic integrated circuit (PIC) can perform one-dimensional spectroscopy for unique input spectrums using a single baseline within its 91 available baselines. The PIC uses polarization diversifying gratings to separate incoming light into two distinct polar… ▽ More This paper reports on the design, fabrication, and demonstration of a silicon photonics based heterodyne interferometric imaging system. The photonic integrated circuit (PIC) can perform one-dimensional spectroscopy for unique input spectrums using a single baseline within its 91 available baselines. The PIC uses polarization diversifying gratings to separate incoming light into two distinct polarizations, an on-chip 2x4 optical hybrid, and a strong local oscillator (LO) to perform the heterodyne measurements. The optical hybrids combine the input signals with the LO and splitting them into 2 components pairs for phase sensitive measurements. Furthermore, the PIC can perform 2-D image reconstruction by combining many baseline pairs to measure the visibility of a simple target. These demonstrations show the PIC's capabilities for 1-D spectroscopy and 2-D imaging applications. △ Less

Submitted 3 April, 2026; originally announced April 2026.

Comments: 24 pages, 8 figures. Submitted to ACS Photonics

A High-frequency Geodetic VLBI Experiment for Optical Clock Comparison

Authors: Monia Negusini, Myoung-Sun Heo, Cecilia Clivati, Shuangjing Xu, Roberto Ricci, Taehyun Jung, Buseung Cho, Matteo Stagni, Claudio Bortolotti, Giuseppe Maccaferri, Federico Perini, Mauro Roma, Do-Young Byun, Do-Heung Je, Marco Pizzocaro, Davide Calonico, Elena Cantoni, Giancarlo Cerretto, Stefano Condio, Giovanni A. Costanzo, Simone Donadello, Irene Goti, Michele Gozzelino, Alberto Mura, Filippo Levi , et al. (18 additional authors not shown)

Abstract: An intercontinental metrological clock comparison between Italy and the Republic of Korea was performed by means of geodetic K-band VLBI observations. The comparison involved the hydrogen masers (H-masers) used at Medicina and Sejong radio telescopes. The same clocks were simultaneously compared by a satellite link and by high-precision optical clocks maintained at the National Metrology Institute… ▽ More An intercontinental metrological clock comparison between Italy and the Republic of Korea was performed by means of geodetic K-band VLBI observations. The comparison involved the hydrogen masers (H-masers) used at Medicina and Sejong radio telescopes. The same clocks were simultaneously compared by a satellite link and by high-precision optical clocks maintained at the National Metrology Institutes, KRISS in Korea and INRIM in Italy, and delivered to VLBI antennas via optical fiber. The H-masers frequency difference was estimated by extrapolating the clock rate from VLBI data using two geodetic VLBI software. This was subsequently compared with clock differences derived by satellite link and by local optical clocks. Results obtained with different approaches were in agreement at the level of $10^{-15}$ s/s. This pilot study demonstrates that standard high-frequency (K-band) geodetic VLBI campaigns could be a viable approach to conduct intercontinental clock comparisons, now only possible via satellite links. This uncertainty can be reduced thanks to the planned installation of new-generation, broadband, high-frequency receivers on the involved telescopes. K/Q/W-band geodetic observations will allow an improvement of the accuracy of the resulting group delays through broad bandwidth synthesis from 20 to 100 GHz. Furthermore, the Frequency Phase Transfer (FPT) method will also be explored together with the use of PCAL systems installed at the radio telescopes to improve phase stability and thus allow a better estimation of the station clock parameters. △ Less

Submitted 24 March, 2026; originally announced March 2026.

Comments: 12 pages, 3 figures

Journal ref: PASP 138 024504 (2026)

TPCpp-10M: Simulated proton-proton collisions in a Time Projection Chamber for AI Foundation Models

Authors: Shuhang Li, Yi Huang, David Park, Xihaier Luo, Haiwang Yu, Yeonju Go, Christopher Pinkenburg, Yuewei Lin, Shinjae Yoo, Joseph Osborn, Christof Roland, Jin Huang, Yihui Ren

Abstract: Scientific foundation models hold great promise for advancing nuclear and particle physics by improving analysis precision and accelerating discovery. Yet, progress in this field is often limited by the lack of openly available large scale datasets, as well as standardized evaluation tasks and metrics. Furthermore, the specialized knowledge and software typically required to process particle physi… ▽ More Scientific foundation models hold great promise for advancing nuclear and particle physics by improving analysis precision and accelerating discovery. Yet, progress in this field is often limited by the lack of openly available large scale datasets, as well as standardized evaluation tasks and metrics. Furthermore, the specialized knowledge and software typically required to process particle physics data pose significant barriers to interdisciplinary collaboration with the broader machine learning community. This work introduces a large, openly accessible dataset of 10 million simulated proton-proton collisions, designed to support self-supervised training of foundation models. To facilitate ease of use, the dataset is provided in a common NumPy format. In addition, it includes 70,000 labeled examples spanning three well defined downstream tasks: track finding, particle identification, and noise tagging, to enable systematic evaluation of the foundation model's adaptability. The simulated data are generated using the Pythia Monte Carlo event generator at a center of mass energy of sqrt(s) = 200 GeV and processed with Geant4 to include realistic detector conditions and signal emulation in the sPHENIX Time Projection Chamber at the Relativistic Heavy Ion Collider, located at Brookhaven National Laboratory. This dataset resource establishes a common ground for interdisciplinary research, enabling machine learning scientists and physicists alike to explore scaling behaviors, assess transferability, and accelerate progress toward foundation models in nuclear and high energy physics. The complete simulation and reconstruction chain is reproducible with the sPHENIX software stack. All data and code locations are provided under Data Accessibility. △ Less

Submitted 6 September, 2025; originally announced September 2025.

Mixed Precision Photonic Computing with 3D Electronic-Photonic Integrated Circuits

Authors: Georgios Charalampous, Rui Chen, Mehmet Berkay On, Aslan Nasirov, Chun-Yi Cheng, Mahmoud AbdelGhany, Arka Majumdar, Ji Wang, Jennifer A. Black, Rajkumar Chinnakonda Kubendran, Caglar Oskay, Zhaojun Bai, Sam Palermo, Scott B. Papp, S. J. Ben Yoo

Abstract: We propose advancing photonic in-memory computing through three-dimensional photonic-electronic integrated circuits using phase-change materials (PCM) and AlGaAs-CMOS technology. These circuits offer high precision (greater than 12 bits), scalability (greater than 1024 by 1024), and massive parallelism (greater than 1 million operations) across the wavelength, spatial, and temporal domains at ultr… ▽ More We propose advancing photonic in-memory computing through three-dimensional photonic-electronic integrated circuits using phase-change materials (PCM) and AlGaAs-CMOS technology. These circuits offer high precision (greater than 12 bits), scalability (greater than 1024 by 1024), and massive parallelism (greater than 1 million operations) across the wavelength, spatial, and temporal domains at ultra-low power (less than 1 watt per PetaOPS). Monolithically integrated hybrid PCM-AlGaAs memory resonators handle coarse-precision iterations (greater than 5-bit most significant bit precision) through reversible PCM phase transitions. Electro-optic memristive tuning enables fine-precision updates (greater than 8-bit least significant bit precision), resulting in over 12-bit precision for in-memory computing. The use of low-loss PCM (less than 0.01 dB per cm) and electro-optical tuning yields memristive optical resonators with high Q-factors (greater than 1 million), low insertion loss, and low tuning power. A W by W photonic tensor core composed of PCM-AlGaAs memresonators performs general matrix multiplication (GEMM) across W wavelengths from optical frequency combs, with minimal crosstalk and loss. Hierarchical scaling in the wavelength domain (K) and spatial domain (L) enables this system to address high-dimensional (N) scientific partial differential equation (PDE) problems in a single constant-time operation, compared to the conventional quadratic-time (N squared) computational complexity. △ Less

Submitted 5 August, 2025; originally announced August 2025.

Unveiling Nano-scale Crystal Deformation using Coherent X-ray Dynamical Diffraction

Authors: Longlong Wu, David Yang, Wei Wang, Shinjae Yoo, Ross J. Harder, Wonsuk Cha, Aiguo Li, Ian K. Robinson

Abstract: Visualization of internal deformation fields in crystalline materials helps bridge the gap between theoretical models and practical applications. Applying Bragg coherent diffraction imaging under X-ray dynamical diffraction conditions provides a promising approach to the longstanding challenge of investigating the deformation fields in micron-sized crystals. Here, we present an automatic different… ▽ More Visualization of internal deformation fields in crystalline materials helps bridge the gap between theoretical models and practical applications. Applying Bragg coherent diffraction imaging under X-ray dynamical diffraction conditions provides a promising approach to the longstanding challenge of investigating the deformation fields in micron-sized crystals. Here, we present an automatic differentiation-based reconstruction method that integrates dynamical scattering theory to accurately reconstruct deformation fields in large crystals. Using this forward model, our simulated and experimental results demonstrate that three-dimensional local strain information inside a large crystal can be accurately reconstructed under coherent X-ray dynamical diffraction conditions with Bragg coherent X-ray diffraction imaging. These findings open an avenue for extending the investigation of local deformation fields to microscale crystals while maintaining nanoscale resolution, leveraging the enhanced coherence and brightness of advanced X-ray sources. △ Less

Submitted 25 December, 2025; v1 submitted 18 June, 2025; originally announced June 2025.

Radio-Frequency Pseudo-Null Induced by Light in an Ion Trap

Authors: Daun Chung, Yonghwan Cha, Hosung Shon, Jeonghyun Park, Woojun Lee, Kyungmin Lee, Beomgeun Cho, Kwangyeul Choi, Chiyoon Kim, Seungwoo Yoo, Suhan Kim, Uihwan Jeong, Jiyong Kang, Jaehun You, Taehyun Kim

Abstract: In a linear radio-frequency (rf) ion trap, the rf null is the point of zero electric field in the dynamic trapping potential where the ion motion is approximately harmonic. When displaced from the rf null, the ion is superimposed by fast oscillations known as micromotion, which can be probed through motion-sensitive light-atom interactions. In this work, we report on the emergence of the rf pseudo… ▽ More In a linear radio-frequency (rf) ion trap, the rf null is the point of zero electric field in the dynamic trapping potential where the ion motion is approximately harmonic. When displaced from the rf null, the ion is superimposed by fast oscillations known as micromotion, which can be probed through motion-sensitive light-atom interactions. In this work, we report on the emergence of the rf pseudo-null, a locus of points where the ion responds to light as if it were at the true rf null, despite being displaced from it. The phenomenon is fully explained by accounting for the general two-dimensional structure of micromotion and is experimentally verified under various potential configurations, with observations in great agreement with numerical simulations. The rf pseudo-null manifests as a line in a two-dimensional parameter space, determined by the geometry of the incident light and its overlap with the motional structure of the ion. The true rf null occurs uniquely at the concurrent point of the pseudo-null lines induced by different light sources. △ Less

Submitted 18 April, 2025; originally announced April 2025.

Journal ref: Phys. Rev. A 112, 013115 (2025)

DeepWheel: Generating a 3D Synthetic Wheel Dataset for Design and Performance Evaluation

Authors: Soyoung Yoo, Namwoo Kang

Abstract: Data-driven design is emerging as a powerful strategy to accelerate engineering innovation. However, its application to vehicle wheel design remains limited due to the lack of large-scale, high-quality datasets that include 3D geometry and physical performance metrics. To address this gap, this study proposes a synthetic design-performance dataset generation framework using generative AI. The prop… ▽ More Data-driven design is emerging as a powerful strategy to accelerate engineering innovation. However, its application to vehicle wheel design remains limited due to the lack of large-scale, high-quality datasets that include 3D geometry and physical performance metrics. To address this gap, this study proposes a synthetic design-performance dataset generation framework using generative AI. The proposed framework first generates 2D rendered images using Stable Diffusion, and then reconstructs the 3D geometry through 2.5D depth estimation. Structural simulations are subsequently performed to extract engineering performance data. To further expand the design and performance space, topology optimization is applied, enabling the generation of a more diverse set of wheel designs. The final dataset, named DeepWheel, consists of over 6,000 photo-realistic images and 900 structurally analyzed 3D models. This multi-modal dataset serves as a valuable resource for surrogate model training, data-driven inverse design, and design space exploration. The proposed methodology is also applicable to other complex design domains. The dataset is released under the Creative Commons Attribution-NonCommercial 4.0 International(CC BY-NC 4.0) and is available on the https://www.smartdesignlab.org/datasets △ Less

Submitted 16 April, 2025; v1 submitted 15 April, 2025; originally announced April 2025.

Comments: 28 pages, 18 figures. Not yet submitted to a journal or conference

MSC Class: 68T07

Generalizable Implicit Neural Representations via Parameterized Latent Dynamics for Baroclinic Ocean Forecasting

Authors: Guang Zhao, Xihaier Luo, Seungjun Lee, Yihui Ren, Shinjae Yoo, Luke Van Roekel, Balu Nadiga, Sri Hari Krishna Narayanan, Yixuan Sun, Wei Xu

Abstract: Mesoscale ocean dynamics play a critical role in climate systems, governing heat transport, hurricane genesis, and drought patterns. However, simulating these processes at high resolution remains computationally prohibitive due to their nonlinear, multiscale nature and vast spatiotemporal domains. Implicit neural representations (INRs) reduce the computational costs as resolution-independent surro… ▽ More Mesoscale ocean dynamics play a critical role in climate systems, governing heat transport, hurricane genesis, and drought patterns. However, simulating these processes at high resolution remains computationally prohibitive due to their nonlinear, multiscale nature and vast spatiotemporal domains. Implicit neural representations (INRs) reduce the computational costs as resolution-independent surrogates but fail in many-query scenarios (inverse modeling) requiring rapid evaluations across diverse parameters. We present PINROD, a novel framework combining dynamics-aware implicit neural representations with parameterized neural ordinary differential equations to address these limitations. By integrating parametric dependencies into latent dynamics, our method efficiently captures nonlinear oceanic behavior across varying boundary conditions and physical parameters. Experiments on ocean mesoscale activity data show superior accuracy over existing baselines and improved computational efficiency compared to standard numerical simulations. △ Less

Submitted 27 March, 2025; originally announced March 2025.

Roadmap on Neuromorphic Photonics

Authors: Daniel Brunner, Bhavin J. Shastri, Mohammed A. Al Qadasi, H. Ballani, Sylvain Barbay, Stefano Biasi, Peter Bienstman, Simon Bilodeau, Wim Bogaerts, Fabian Böhm, G. Brennan, Sonia Buckley, Xinlun Cai, Marcello Calvanese Strinati, B. Canakci, Benoit Charbonnier, Mario Chemnitz, Yitong Chen, Stanley Cheung, Jeff Chiles, Suyeon Choi, Demetrios N. Christodoulides, Lukas Chrostowski, J. Chu, J. H. Clegg , et al. (125 additional authors not shown)

Abstract: This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementation philosophies reported in the field. It emphasizes the critical role of cross-disciplinary collaboration in this rapidly evolving field. This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementation philosophies reported in the field. It emphasizes the critical role of cross-disciplinary collaboration in this rapidly evolving field. △ Less

Submitted 16 January, 2025; v1 submitted 14 January, 2025; originally announced January 2025.

Variable Rate Neural Compression for Sparse Detector Data

Authors: Yi Huang, Yeonju Go, Jin Huang, Shuhang Li, Xihaier Luo, Thomas Marshall, Joseph Osborn, Christopher Pinkenburg, Yihui Ren, Evgeny Shulga, Shinjae Yoo, Byung-Jun Yoon

Abstract: High-energy large-scale particle colliders generate data at extraordinary rates. Developing real-time high-throughput data compression algorithms to reduce data volume and meet the bandwidth requirement for storage has become increasingly critical. Deep learning is a promising technology that can address this challenging topic. At the newly constructed sPHENIX experiment at the Relativistic Heavy… ▽ More High-energy large-scale particle colliders generate data at extraordinary rates. Developing real-time high-throughput data compression algorithms to reduce data volume and meet the bandwidth requirement for storage has become increasingly critical. Deep learning is a promising technology that can address this challenging topic. At the newly constructed sPHENIX experiment at the Relativistic Heavy Ion Collider, a Time Projection Chamber (TPC) serves as the main tracking detector, which records three-dimensional particle trajectories in a volume of a gas-filled cylinder. In terms of occupancy, the resulting data flow can be very sparse reaching $10^{-3}$ for proton-proton collisions. Such sparsity presents a challenge to conventional learning-free lossy compression algorithms, such as SZ, ZFP, and MGARD. In contrast, emerging deep learning-based models, particularly those utilizing convolutional neural networks for compression, have outperformed these conventional methods in terms of compression ratios and reconstruction accuracy. However, research on the efficacy of these deep learning models in handling sparse datasets, like those produced in particle colliders, remains limited. Furthermore, most deep learning models do not adapt their processing speeds to data sparsity, which affects efficiency. To address this issue, we propose a novel approach for TPC data compression via key-point identification facilitated by sparse convolution. Our proposed algorithm, BCAE-VS, achieves a $75\%$ improvement in reconstruction accuracy with a $10\%$ increase in compression ratio over the previous state-of-the-art model. Additionally, BCAE-VS manages to achieve these results with a model size over two orders of magnitude smaller. Lastly, we have experimentally verified that as sparsity increases, so does the model's throughput. △ Less

Submitted 18 November, 2024; originally announced November 2024.

Comments: 37 pages, 12 figures, submitted to Journal of Computational Physics

Towards Reverse-Engineering the Brain: Brain-Derived Neuromorphic Computing Approach with Photonic, Electronic, and Ionic Dynamicity in 3D integrated circuits

Authors: S. J. Ben Yoo, Luis El-Srouji, Suman Datta, Shimeng Yu, Jean Anne Incorvia, Alberto Salleo, Volker Sorger, Juejun Hu, Lionel C Kimerling, Kristofer Bouchard, Joy Geng, Rishidev Chaudhuri, Charan Ranganath, Randall O'Reilly

Abstract: The human brain has immense learning capabilities at extreme energy efficiencies and scale that no artificial system has been able to match. For decades, reverse engineering the brain has been one of the top priorities of science and technology research. Despite numerous efforts, conventional electronics-based methods have failed to match the scalability, energy efficiency, and self-supervised lea… ▽ More The human brain has immense learning capabilities at extreme energy efficiencies and scale that no artificial system has been able to match. For decades, reverse engineering the brain has been one of the top priorities of science and technology research. Despite numerous efforts, conventional electronics-based methods have failed to match the scalability, energy efficiency, and self-supervised learning capabilities of the human brain. On the other hand, very recent progress in the development of new generations of photonic and electronic memristive materials, device technologies, and 3D electronic-photonic integrated circuits (3D EPIC ) promise to realize new brain-derived neuromorphic systems with comparable connectivity, density, energy-efficiency, and scalability. When combined with bio-realistic learning algorithms and architectures, it may be possible to realize an 'artificial brain' prototype with general self-learning capabilities. This paper argues the possibility of reverse-engineering the brain through architecting a prototype of a brain-derived neuromorphic computing system consisting of artificial electronic, ionic, photonic materials, devices, and circuits with dynamicity resembling the bio-plausible molecular, neuro/synaptic, neuro-circuit, and multi-structural hierarchical macro-circuits of the brain based on well-tested computational models. We further argue the importance of bio-plausible local learning algorithms applicable to the neuromorphic computing system that capture the flexible and adaptive unsupervised and self-supervised learning mechanisms central to human intelligence. Most importantly, we emphasize that the unique capabilities in brain-derived neuromorphic computing prototype systems will enable us to understand links between specific neuronal and network-level properties with system-level functioning and behavior. △ Less

Submitted 28 March, 2024; originally announced March 2024.

Comments: 15 pages, 12 figures

Experimental Demonstration of Imperfection-Agnostic Local Learning Rules on Photonic Neural Networks with Mach-Zehnder Interferometric Meshes

Authors: Luis El Srouji, Mehmet Berkay On, Yun-Jhu Lee, Mahmoud Abdelghany, S. J. Ben Yoo

Abstract: Mach-Zehnder Interferometric meshes are attractive for low-loss photonic matrix multiplication but are challenging to program. Using least-squares optimization of directional derivatives, we experimentally demonstrate that desired matrix updates can be implemented agnostic to hardware imperfections. \c{opyright} 2024 The Author(s) Mach-Zehnder Interferometric meshes are attractive for low-loss photonic matrix multiplication but are challenging to program. Using least-squares optimization of directional derivatives, we experimentally demonstrate that desired matrix updates can be implemented agnostic to hardware imperfections. \c{opyright} 2024 The Author(s) △ Less

Submitted 7 January, 2024; originally announced January 2024.

0.08 fF, 0.72 nA dark current, 91% Quantum Efficiency, 38 Gb/s Nano-photodetector on a 45 nm CMOS Silicon-Photonic Platform

Authors: Mingye Fu, S. J. Ben Yoo

Abstract: We demonstrated a Germanium-on-Silicon photodetector utilizing an asymmetric-Fabry-Perot resonator with 0.08 fF capacitance. The measurements at 1315.5 nm show 0.72 nA (3.40 nA) dark current, 0.93 A/W (0.96 A/W) responsivity, 36 Gb/s (38 Gb/s) operation at -1V (-2V) bias. We demonstrated a Germanium-on-Silicon photodetector utilizing an asymmetric-Fabry-Perot resonator with 0.08 fF capacitance. The measurements at 1315.5 nm show 0.72 nA (3.40 nA) dark current, 0.93 A/W (0.96 A/W) responsivity, 36 Gb/s (38 Gb/s) operation at -1V (-2V) bias. △ Less

Submitted 7 January, 2024; originally announced January 2024.

Dose-efficient Automatic Differentiation for Ptychographic Reconstruction

Authors: Longlong Wu, Shinjae Yoo, Yong S. Chu, Xiaojing Huang, Ian K. Robinson

Abstract: Ptychography, as a powerful lensless imaging method, has become a popular member of the coherent diffractive imaging family over decades of development. The ability to utilize low-dose X-rays and/or fast scans offers a big advantage in a ptychographic measurement (for example, when measuring radiation-sensitive samples), but results in low-photon statistics, making the subsequent phase retrieval c… ▽ More Ptychography, as a powerful lensless imaging method, has become a popular member of the coherent diffractive imaging family over decades of development. The ability to utilize low-dose X-rays and/or fast scans offers a big advantage in a ptychographic measurement (for example, when measuring radiation-sensitive samples), but results in low-photon statistics, making the subsequent phase retrieval challenging. Here, we demonstrate a dose-efficient automatic differentiation framework for ptychographic reconstruction (DAP) at low-photon statistics and low overlap ratio. As no reciprocal space constraint is required in this DAP framework, the framework, based on various forward models, shows superior performance under these conditions. It effectively suppresses potential artifacts in the reconstructed images, especially for the inherent periodic artifact in a raster scan. We validate the effectiveness and robustness of this method using both simulated and measured datasets. △ Less

Submitted 12 June, 2024; v1 submitted 3 January, 2024; originally announced January 2024.

Comments: 26 pages, 5 figures

Journal ref: Optica 11, 821-830 (2024)

ITRUSST Consensus on Biophysical Safety for Transcranial Ultrasound Stimulation

Authors: Jean-François Aubry, David Attali, Mark E. Schafer, Elsa Fouragnan, Charles F. Caskey, Robert Chen, Ghazaleh Darmani, Ellen J. Bubrick, Jérôme Sallet, Christopher R. Butler, Charlotte J. Stagg, Miriam C. Klein-Flügge, Seung-Schik Yoo, Christy Holland, Brad Treeby, Eleanor Martin, Lennart Verhagen, Kim Butts Pauly

Abstract: Transcranial ultrasound stimulation (TUS) is an emerging technology for non-invasive brain stimulation. The International Transcranial Ultrasonic Stimulation Safety and Standards consortium (ITRUSST) has established consensus on considerations for nonsignificant biophysical risk of TUS, drawing upon the literature and established regulations for biomedical devices. Here, we assume the application… ▽ More Transcranial ultrasound stimulation (TUS) is an emerging technology for non-invasive brain stimulation. The International Transcranial Ultrasonic Stimulation Safety and Standards consortium (ITRUSST) has established consensus on considerations for nonsignificant biophysical risk of TUS, drawing upon the literature and established regulations for biomedical devices. Here, we assume the application of TUS to individuals without contraindications, compromised thermoregulation, vascular vulnerabilities, or administered ultrasound contrast agents. In this context, we present a concise yet comprehensive set of levels for nonsignificant risks of TUS application. For mechanical effects, it is non-significant risk if the mechanical index (MI) or the mechanical index for transcranial application (MItc) does not exceed 1.9. For thermal effects, it is non-significant risk if any of the following three levels are met: the peak temperature rise does not exceed 2°C or the peak absolute temperature does not exceed 39°C, assuming a baseline temperature of 37°C, the thermal dose does not exceed 2 CEM43 in brain tissue, 16 CEM43 in bone tissue, and 21 CEM43 in skin tissue, or specific values of the thermal index (TI) for a given exposure time. This report reflects a consensus expert opinion and can inform, but not replace, regulatory guidelines or official international standards. Similarly, this consensus can inform, but not replace, ethical evaluation, which weighs the total burden, risks, and benefits of the proposed action. The stated levels are not safety limits per se, and further data is needed to establish the threshold for significant risk. We review literature relevant to our considerations and discuss limitations and future developments of our approach. △ Less

Submitted 8 October, 2025; v1 submitted 9 November, 2023; originally announced November 2023.

Comments: ITRUSST consensus, 16 pages, 1 table, 1 figure

Emulating Expert Insight: A Robust Strategy for Optimal Experimental Design

Authors: Matthew R. Carbone, Hyeong Jin Kim, Chandima Fernando, Shinjae Yoo, Daniel Olds, Howie Joress, Brian DeCost, Bruce Ravel, Yugang Zhang, Phillip M. Maffettone

Abstract: The challenge of optimal design of experiments (DOE) pervades materials science, physics, chemistry, and biology. Bayesian optimization has been used to address this challenge in vast sample spaces, although it requires framing experimental campaigns through the lens of maximizing some observable. This framing is insufficient for epistemic research goals that seek to comprehensively analyze a samp… ▽ More The challenge of optimal design of experiments (DOE) pervades materials science, physics, chemistry, and biology. Bayesian optimization has been used to address this challenge in vast sample spaces, although it requires framing experimental campaigns through the lens of maximizing some observable. This framing is insufficient for epistemic research goals that seek to comprehensively analyze a sample space, without an explicit scalar objective (e.g., the characterization of a wafer or sample library). In this work, we propose a flexible formulation of scientific value that recasts a dataset of input conditions and higher-dimensional observable data into a continuous, scalar metric. Intuitively, the scientific value function measures where observables change significantly, emulating the perspective of experts driving an experiment, and can be used in collaborative analysis tools or as an objective for optimization techniques. We demonstrate this technique by exploring simulated phase boundaries from different observables, autonomously driving a variable temperature measurement of a ferroelectric material, and providing feedback from a nanoparticle synthesis campaign. The method is seamlessly compatible with existing optimization tools, can be extended to multi-modal and multi-fidelity experiments, and can integrate existing models of an experimental system. Because of its flexibility, it can be deployed in a range of experimental settings for autonomous or accelerated experiments. △ Less

Submitted 25 July, 2023; originally announced July 2023.

Programmable Integrated Photonics for Topological Hamiltonians

Authors: Mehmet Berkay On, Farshid Ashtiani, David Sanchez-Jacome, Daniel Perez-Lopez, S. J. Ben Yoo, Andrea Blanco-Redondo

Abstract: A variety of topological Hamiltonians have been demonstrated in photonic platforms, leading to fundamental discoveries and enhanced robustness in applications such as lasing, sensing, and quantum technologies. To date, each topological photonic platform implements a specific type of Hamiltonian with inexistent or limited reconfigurability. Here, we propose and demonstrate different topological mod… ▽ More A variety of topological Hamiltonians have been demonstrated in photonic platforms, leading to fundamental discoveries and enhanced robustness in applications such as lasing, sensing, and quantum technologies. To date, each topological photonic platform implements a specific type of Hamiltonian with inexistent or limited reconfigurability. Here, we propose and demonstrate different topological models by using the same reprogrammable integrated photonics platform, consisting of a hexagonal mesh of silicon Mach-Zehnder interferometers with phase-shifters. We specifically demonstrate a one-dimensional Su-Schrieffer-Heeger Hamiltonian supporting a localized topological edge mode and a higher-order topological insulator based on a two-dimensional breathing Kagome Hamiltonian with three corner states. These results highlight a nearly universal platform for topological models that may fast-track research progress toward applications of topological photonics and other coupled systems. △ Less

Submitted 10 July, 2023; originally announced July 2023.

Optimal sensor placement for reconstructing wind pressure field around buildings using compressed sensing

Authors: Xihaier Luo, Ahsan Kareem, Shinjae Yoo

Abstract: Deciding how to optimally deploy sensors in a large, complex, and spatially extended structure is critical to ensure that the surface pressure field is accurately captured for subsequent analysis and design. In some cases, reconstruction of missing data is required in downstream tasks such as the development of digital twins. This paper presents a data-driven sparse sensor selection algorithm, aim… ▽ More Deciding how to optimally deploy sensors in a large, complex, and spatially extended structure is critical to ensure that the surface pressure field is accurately captured for subsequent analysis and design. In some cases, reconstruction of missing data is required in downstream tasks such as the development of digital twins. This paper presents a data-driven sparse sensor selection algorithm, aiming to provide the most information contents for reconstructing aerodynamic characteristics of wind pressures over tall building structures parsimoniously. The algorithm first fits a set of basis functions to the training data, then applies a computationally efficient QR algorithm that ranks existing pressure sensors in order of importance based on the state reconstruction to this tailored basis. The findings of this study show that the proposed algorithm successfully reconstructs the aerodynamic characteristics of tall buildings from sparse measurement locations, generating stable and optimal solutions across a range of conditions. As a result, this study serves as a promising first step toward leveraging the success of data-driven and machine learning algorithms to supplement traditional genetic algorithms currently used in wind engineering. △ Less

Submitted 7 June, 2023; originally announced June 2023.

Comments: 31 pages, 19 figures

Journal ref: Journal of Building Engineering, p.106855 (2023)

Quantum Wrapper Networking

Authors: S. J. Ben Yoo, Sandeep Kumar Singh, Mehmet Berkay On, Gamze Gul, Gregory S. Kanter, Roberto Proietti, Prem Kumar

Abstract: We introduce a new concept of Quantum Wrapper Networking, which enables control, management, and operation of quantum networks that can co-exist with classical networks while keeping the requirements for quantum networks intact. The quantum wrapper networks (QWNs) enable the transparent and interoperable transportation of quantum wrapper datagrams consisting of quantum payloads and, notably, class… ▽ More We introduce a new concept of Quantum Wrapper Networking, which enables control, management, and operation of quantum networks that can co-exist with classical networks while keeping the requirements for quantum networks intact. The quantum wrapper networks (QWNs) enable the transparent and interoperable transportation of quantum wrapper datagrams consisting of quantum payloads and, notably, classical headers to facilitate the datagram switching without measuring or disturbing the qubits of the quantum payload. Furthermore, QWNs can utilize the common network control and management for performance monitoring on the classical header and infer the quantum channel quality. △ Less

Submitted 30 April, 2023; originally announced May 2023.

Comments: This manuscript has 5 figures and 7 pages. The very early version of the initial concept of Quantum Wrapper Networking was published in IEEE Photonics Conference 2021 followed by Optical Communications Conference 2023 Paper Tu3H.4 (referenced as [12])

Evaluating the Transferability of Machine-Learned Force Fields for Material Property Modeling

Authors: Shaswat Mohanty, Sanghyuk Yoo, Keonwook Kang, Wei Cai

Abstract: Machine-learned force fields have generated significant interest in recent years as a tool for molecular dynamics (MD) simulations, with the aim of developing accurate and efficient models that can replace classical interatomic potentials. However, before these models can be confidently applied to materials simulations, they must be thoroughly tested and validated. The existing tests on the radial… ▽ More Machine-learned force fields have generated significant interest in recent years as a tool for molecular dynamics (MD) simulations, with the aim of developing accurate and efficient models that can replace classical interatomic potentials. However, before these models can be confidently applied to materials simulations, they must be thoroughly tested and validated. The existing tests on the radial distribution function and mean-squared displacements are insufficient in assessing the transferability of these models. Here we present a more comprehensive set of benchmarking tests for evaluating the transferability of machine-learned force fields. We use a graph neural network (GNN)-based force field coupled with the OpenMM package to carry out MD simulations for Argon as a test case. Our tests include computational X-ray photon correlation spectroscopy (XPCS) signals, which capture the density fluctuation at various length scales in the liquid phase, as well as phonon density-of-state in the solid phase and the liquid-solid phase transition behavior. Our results show that the model can accurately capture the behavior of the solid phase only when the configurations from the solid phase are included in the training dataset. This underscores the importance of appropriately selecting the training data set when developing machine-learned force fields. The tests presented in this work provide a necessary foundation for the development and application of machine-learned force fields for materials simulations. △ Less

Submitted 15 January, 2023; v1 submitted 9 January, 2023; originally announced January 2023.

Comments: 27 pages, 14 figures, under review

Charged Particle Tracking with Machine Learning on FPGAs

Authors: H. Abidi, A. Boveia, V. Cavaliere, D. Furletov, A. Gekow, C. W. Kalderon, S. Yoo

Abstract: The determination of charged particle trajectories (tracking) in collisions at the CERN Large Hadron Collider (LHC) is one of the most important aspects for event reconstruction at hadron colliders. This is especially true in the high conditions expected during the future high-luminosity phase of the LHC (HL-LHC) where the number of interactions per beam crossing will increase by a factor of five.… ▽ More The determination of charged particle trajectories (tracking) in collisions at the CERN Large Hadron Collider (LHC) is one of the most important aspects for event reconstruction at hadron colliders. This is especially true in the high conditions expected during the future high-luminosity phase of the LHC (HL-LHC) where the number of interactions per beam crossing will increase by a factor of five. Deep learning algorithms have been successfully applied to this task for offline applications. However, their study in hardware-based trigger applications has been limited . In this paper, we study different algorithms for two different steps of tracking and show that such algorithms can be run on field-programmable gate arrays (FPGAs). △ Less

Submitted 5 December, 2022; originally announced December 2022.

A Deep Finite Difference Emulator for the Fast Simulation of Coupled Viscous Burgers' Equation

Authors: Xihaier Luo, Yihui Ren, Wei Xu, Shinjae Yoo, Balasubramanya Nadiga, Ahsan Kareem

Abstract: This work proposes a deep learning-based emulator for the efficient computation of the coupled viscous Burgers' equation with random initial conditions. In a departure from traditional data-driven deep learning approaches, the proposed emulator does not require a classical numerical solver to collect training data. Instead, it makes direct use of the problem's physics. Specifically, the model emul… ▽ More This work proposes a deep learning-based emulator for the efficient computation of the coupled viscous Burgers' equation with random initial conditions. In a departure from traditional data-driven deep learning approaches, the proposed emulator does not require a classical numerical solver to collect training data. Instead, it makes direct use of the problem's physics. Specifically, the model emulates a second-order finite difference solver, i.e., the Crank-Nicolson scheme in learning dynamics. A systematic case study is conducted to examine the model's prediction performance, generalization ability, and computational efficiency. The computed results are graphically represented and compared to those of state-of-the-art numerical solvers. △ Less

Submitted 23 February, 2022; originally announced February 2022.

Comments: 23 pages, 8 figures

A Bayesian Deep Learning Approach to Near-Term Climate Prediction

Authors: Xihaier Luo, Balasubramanya T. Nadiga, Yihui Ren, Ji Hwan Park, Wei Xu, Shinjae Yoo

Abstract: Since model bias and associated initialization shock are serious shortcomings that reduce prediction skills in state-of-the-art decadal climate prediction efforts, we pursue a complementary machine-learning-based approach to climate prediction. The example problem setting we consider consists of predicting natural variability of the North Atlantic sea surface temperature on the interannual timesca… ▽ More Since model bias and associated initialization shock are serious shortcomings that reduce prediction skills in state-of-the-art decadal climate prediction efforts, we pursue a complementary machine-learning-based approach to climate prediction. The example problem setting we consider consists of predicting natural variability of the North Atlantic sea surface temperature on the interannual timescale in the pre-industrial control simulation of the Community Earth System Model (CESM2). While previous works have considered the use of recurrent networks such as convolutional LSTMs and reservoir computing networks in this and other similar problem settings, we currently focus on the use of feedforward convolutional networks. In particular, we find that a feedforward convolutional network with a Densenet architecture is able to outperform a convolutional LSTM in terms of predictive skill. Next, we go on to consider a probabilistic formulation of the same network based on Stein variational gradient descent and find that in addition to providing useful measures of predictive uncertainty, the probabilistic (Bayesian) version improves on its deterministic counterpart in terms of predictive skill. Finally, we characterize the reliability of the ensemble of ML models obtained in the probabilistic setting by using analysis tools developed in the context of ensemble numerical weather prediction. △ Less

Submitted 22 February, 2022; originally announced February 2022.

Comments: 32 pages, 12 figures

A Machine Learning-based Characterization Framework for Parametric Representation of Nonlinear Sloshing

Authors: Xihaier Luo, Ahsan Kareem, Liting Yu, Shinjae Yoo

Abstract: The growing interest in creating a parametric representation of liquid sloshing inside a container stems from its practical applications in modern engineering systems. The resonant excitation, on the other hand, can cause unstable and nonlinear water waves, resulting in chaotic motions and non-Gaussian signals. This paper presents a novel machine learning-based framework for nonlinear liquid slosh… ▽ More The growing interest in creating a parametric representation of liquid sloshing inside a container stems from its practical applications in modern engineering systems. The resonant excitation, on the other hand, can cause unstable and nonlinear water waves, resulting in chaotic motions and non-Gaussian signals. This paper presents a novel machine learning-based framework for nonlinear liquid sloshing representation learning. The proposed method is a parametric modeling technique that is based on sequential learning and sparse regularization. The dynamics are categorized into two parts: linear evolution and nonlinear forcing. The former advances the dynamical system in time on an embedded manifold, while the latter causes divergent behaviors in temporal evolution, such as bursting and switching. The proposed framework's merit is demonstrated using an experimental dataset of liquid sloshing in a tank under horizontal excitation with a wide frequency range and various vertical slat screen settings. △ Less

Submitted 25 January, 2022; originally announced January 2022.

Comments: 30 pages, 13 figures

Efficient Fizeau Drag from Dirac electrons in monolayer graphene

Authors: Wenyu Zhao, Sihan Zhao, Hongyuan Li, Sheng Wang, Shaoxin Wang, M. Iqbal Bakti Utama, Salman Kahn, Yue Jiang, Xiao Xiao, SeokJae Yoo, Kenji Watanabe, Takashi Taniguchi, Alex Zettl, Feng Wang

Abstract: Fizeau demonstrated in 1850 that the speed of light can be modified when it is propagating in moving media. Can we achieve such control of the light speed efficiently with a fast-moving electron media by passing electrical current? Because the strong electromagnetic coupling between the electron and light leads to the collective excitation of plasmon polaritons, it will manifest as the plasmonic D… ▽ More Fizeau demonstrated in 1850 that the speed of light can be modified when it is propagating in moving media. Can we achieve such control of the light speed efficiently with a fast-moving electron media by passing electrical current? Because the strong electromagnetic coupling between the electron and light leads to the collective excitation of plasmon polaritons, it will manifest as the plasmonic Doppler effect. Experimental observation of the plasmonic Doppler effect in electronic system has been challenge because the plasmon propagation speed is much faster than the electron drift velocity in conventional noble metals. Here, we report direct observation of Fizeau drag of plasmon polaritons in strongly biased graphene by exploiting the high electron mobility and the slow plasmon propagation of massless Dirac electrons. The large bias current in graphene creates a fast drifting Dirac electron medium hosting the plasmon polariton. It results in nonreciprocal plasmon propagation, where plasmons moving with the drifting electron media propagate at an enhanced speed. We measure the Doppler-shifted plasmon wavelength using a cryogenic near-field infrared nanoscopy, which directly images the plasmon polariton mode in the biased graphene at low temperature. We observe a plasmon wavelength difference up to 3.6% between plasmon moving along and against the drifting electron media. Our findings on the plasmonic Doppler effect open new opportunities for electrical control of non-reciprocal surface plasmon polaritons in nonequilibrium systems. △ Less

Submitted 3 June, 2021; originally announced June 2021.

Comments: 31 pages, 11 figures

Izhikevich-Inspired Optoelectronic Neurons with Excitatory and Inhibitory Inputs for Energy-Efficient Photonic Spiking Neural Networks

Authors: Yun-jhu Lee, Mehmet Berkay On, Xian Xiao, Roberto Proietti, S. J. Ben Yoo

Abstract: We designed, prototyped, and experimentally demonstrated, for the first time to our knowledge, an optoelectronic spiking neuron inspired by the Izhikevich model incorporating both excitatory and inhibitory optical spiking inputs and producing optical spiking outputs accordingly. The optoelectronic neurons consist of three transistors acting as electrical spiking circuits, a vertical-cavity surface… ▽ More We designed, prototyped, and experimentally demonstrated, for the first time to our knowledge, an optoelectronic spiking neuron inspired by the Izhikevich model incorporating both excitatory and inhibitory optical spiking inputs and producing optical spiking outputs accordingly. The optoelectronic neurons consist of three transistors acting as electrical spiking circuits, a vertical-cavity surface-emitting laser (VCSEL) for optical spiking outputs, and two photodetectors for excitatory and inhibitory optical spiking inputs. Additional inclusion of capacitors and resistors complete the Izhikevich-inspired optoelectronic neurons, which receive excitatory and inhibitory optical spikes as inputs from other optoelectronic neurons. We developed a detailed optoelectronic neuron model in Verilog-A and simulated the circuit-level operation of various cases with excitatory input and inhibitory input signals. The experimental results closely resemble the simulated results and demonstrate how the excitatory inputs trigger the optical spiking outputs while the inhibitory inputs suppress the outputs. Utilizing the simulated neuron model, we conducted simulations using fully connected (FC) and convolutional neural networks (CNN). The simulation results using MNIST handwritten digits recognition show 90% accuracy on unsupervised learning and 97% accuracy on a supervised modified FC neural network. We further designed a nanoscale optoelectronic neuron utilizing quantum impedance conversion where a 200 aJ/spike input can trigger the output from on-chip nanolasers with 10 fJ/spike. The nanoscale neuron can support a fanout of ~80 or overcome 19 dB excess optical loss while running at 10 GSpikes/second in the neural network, which corresponds to 100x throughput and 1000x energy-efficiency improvement compared to state-of-art electrical neuromorphic hardware such as Loihi and NeuroGrid. △ Less

Submitted 2 May, 2021; originally announced May 2021.

Comments: 24 pages, 13 figures

Three-dimensional Coherent X-ray Diffraction Imaging via Deep Convolutional Neural Networks

Authors: Longlong Wu, Shinjae Yoo, Ana F. Suzana, Tadesse A. Assefa, Jiecheng Diao, Ross J. Harder, Wonsuk Cha, Ian K. Robinson

Abstract: As a critical component of coherent X-ray diffraction imaging (CDI), phase retrieval has been extensively applied in X-ray structural science to recover the 3D morphological information inside measured particles. Despite meeting all the oversampling requirements of Sayre and Shannon, current phase retrieval approaches still have trouble achieving a unique inversion of experimental data in the pres… ▽ More As a critical component of coherent X-ray diffraction imaging (CDI), phase retrieval has been extensively applied in X-ray structural science to recover the 3D morphological information inside measured particles. Despite meeting all the oversampling requirements of Sayre and Shannon, current phase retrieval approaches still have trouble achieving a unique inversion of experimental data in the presence of noise. Here, we propose to overcome this limitation by incorporating a 3D Machine Learning (ML) model combining (optional) supervised learning with transfer learning. The trained ML model can rapidly provide an immediate result with high accuracy which could benefit real-time experiments, and the predicted result can be further refined with transfer learning. More significantly, the proposed ML model can be used without any prior training to learn the missing phases of an image based on minimization of an appropriate 'loss function' alone. We demonstrate significantly improved performance with experimental Bragg CDI data over traditional iterative phase retrieval algorithms. △ Less

Submitted 19 October, 2021; v1 submitted 26 February, 2021; originally announced March 2021.

Journal ref: npj Comput Mater 7, 175 (2021)

Hybrid Quantum-Classical Graph Convolutional Network

Authors: Samuel Yen-Chi Chen, Tzu-Chieh Wei, Chao Zhang, Haiwang Yu, Shinjae Yoo

Abstract: The high energy physics (HEP) community has a long history of dealing with large-scale datasets. To manage such voluminous data, classical machine learning and deep learning techniques have been employed to accelerate physics discovery. Recent advances in quantum machine learning (QML) have indicated the potential of applying these techniques in HEP. However, there are only limited results in QML… ▽ More The high energy physics (HEP) community has a long history of dealing with large-scale datasets. To manage such voluminous data, classical machine learning and deep learning techniques have been employed to accelerate physics discovery. Recent advances in quantum machine learning (QML) have indicated the potential of applying these techniques in HEP. However, there are only limited results in QML applications currently available. In particular, the challenge of processing sparse data, common in HEP datasets, has not been extensively studied in QML models. This research provides a hybrid quantum-classical graph convolutional network (QGCNN) for learning HEP data. The proposed framework demonstrates an advantage over classical multilayer perceptron and convolutional neural networks in the aspect of number of parameters. Moreover, in terms of testing accuracy, the QGCNN shows comparable performance to a quantum convolutional neural network on the same HEP dataset while requiring less than $50\%$ of the parameters. Based on numerical simulation results, studying the application of graph convolutional operations and other QML models may prove promising in advancing HEP research and other scientific fields. △ Less

Submitted 15 January, 2021; originally announced January 2021.

Quantum Convolutional Neural Networks for High Energy Physics Data Analysis

Authors: Samuel Yen-Chi Chen, Tzu-Chieh Wei, Chao Zhang, Haiwang Yu, Shinjae Yoo

Abstract: This work presents a quantum convolutional neural network (QCNN) for the classification of high energy physics events. The proposed model is tested using a simulated dataset from the Deep Underground Neutrino Experiment. The proposed architecture demonstrates the quantum advantage of learning faster than the classical convolutional neural networks (CNNs) under a similar number of parameters. In ad… ▽ More This work presents a quantum convolutional neural network (QCNN) for the classification of high energy physics events. The proposed model is tested using a simulated dataset from the Deep Underground Neutrino Experiment. The proposed architecture demonstrates the quantum advantage of learning faster than the classical convolutional neural networks (CNNs) under a similar number of parameters. In addition to faster convergence, the QCNN achieves greater test accuracy compared to CNNs. Based on experimental results, it is a promising direction to study the application of QCNN and other quantum machine learning models in high energy physics and additional scientific fields. △ Less

Submitted 22 December, 2020; originally announced December 2020.

Neutron Detection using a Gadolinium-Cathode GEM Detector

Authors: DongHyun Song, Kyungeon Choi, Youngun Jeng, Yechan Kang, Jason Sang Hun Lee, Inkyu Park, Sunyoung Yoo

Abstract: A gas electron multiplier (GEM) detector with a gadolinium cathode has been developed to explore its potential application as a neutron detector. It consists of three standard-sized ($10\times 10$ cm${}^{2}$) GEM foils and a thin gadolinium plate as the cathode, which is used as a neutron converter. The neutron detection efficiencies were measured for two different cathode setups and for two diffe… ▽ More A gas electron multiplier (GEM) detector with a gadolinium cathode has been developed to explore its potential application as a neutron detector. It consists of three standard-sized ($10\times 10$ cm${}^{2}$) GEM foils and a thin gadolinium plate as the cathode, which is used as a neutron converter. The neutron detection efficiencies were measured for two different cathode setups and for two different drift gaps. The thermal neutron source at the Korea Research Institute of Standards and Science (KRISS) was used to measure the neutron detection efficiency. Based on the neutron flux measured by KRISS, the neutron detection efficiency of our gadolinium GEM detector was $4.630 \pm 0.034(stat.) \pm 0.279(syst.) \%$. △ Less

Submitted 4 December, 2020; originally announced December 2020.

Extrinsic Voltage Control of Carrier Lifetime in Polycrystalline PbSe Mid-wave IR Photo Detectors for Increased Detectivity

Authors: Samiran Ganguly, Tang Xin, Sung-Shik Yoo, Philippe Guyot-Sionnest, Avik W. Ghosh

Abstract: Polycrystalline PbSe for mid-wave IR (MWIR) photodetector is an attractive material option due to high operating/ambient temperature operation and relatively easy and cheap fabrication process, making it candidate for low-power and small footprint applications such as internet-of-thing (IoT) sensors and deployment on mobile platforms due to reduced/removed active cooling requirements. However, the… ▽ More Polycrystalline PbSe for mid-wave IR (MWIR) photodetector is an attractive material option due to high operating/ambient temperature operation and relatively easy and cheap fabrication process, making it candidate for low-power and small footprint applications such as internet-of-thing (IoT) sensors and deployment on mobile platforms due to reduced/removed active cooling requirements. However, there are many material challenges that reduce the detectivity of these detectors. In this work, we demonstrate that it is possible to improve upon this metric by externally modulating the lifetime of conducting carriers by application of a back-gate voltage that can control the recombination rate of generated carrier. We first describe the physics of $PbSe$ detectors, the mechanisms underlying carrier transport, and long observed lifetimes of conducting carriers. We then discuss the voltage control of these inverted channels using a back-plane gate resulting in modulation of the lifetime of these carriers. This voltage control represents and extrinsic "knob" through which it may be possible to open a pathway for design of high performance IR photodetectors, as shown in this work. △ Less

Submitted 6 July, 2020; originally announced July 2020.

Comments: 4 figures. In review

Machine-Learning X-ray Absorption Spectra to Quantitative Accuracy

Authors: Matthew R. Carbone, Mehmet Topsakal, Deyu Lu, Shinjae Yoo

Abstract: The advent of massive data repositories has propelled machine learning techniques to the front lines of many scientific fields, and exploring new frontiers by leveraging the predictive power of machine learning will greatly accelerate big data-assisted discovery. In this work, we show that graph-based neural networks can be used to predict the near edge x-ray absorption structure spectra of molecu… ▽ More The advent of massive data repositories has propelled machine learning techniques to the front lines of many scientific fields, and exploring new frontiers by leveraging the predictive power of machine learning will greatly accelerate big data-assisted discovery. In this work, we show that graph-based neural networks can be used to predict the near edge x-ray absorption structure spectra of molecules with exceptional accuracy. The predicted spectra reproduce nearly all the prominent peaks, with 90% of the predicted peak locations within 1 eV of the ground truth. Our study demonstrates that machine learning models can achieve practically the same accuracy as first-principles calculations in predicting complex physical quantities, such as spectral functions, but at a fraction of the cost. △ Less

Submitted 14 February, 2020; v1 submitted 31 December, 2019; originally announced December 2019.

Comments: 6 pages, 3 figures and 1 table

Journal ref: Phys. Rev. Lett. 124, 156401 (2020)

Observation of incoherently coupled dark-bright vector solitons in single-mode fibers

Authors: X. Hu, J. Guo, G. D. Shao, Y. F. Song, S. W. Yoo, B. A. Malomed, D. Y. Tang

Abstract: We report experimental observation of incoherently coupled dark-bright vector solitons in single-mode fibers. Properties of the vector solitons agree well with those predicted by the respective systems of incoherently coupled nonlinear Schroedinger equations. To the best of our knowledge, this is the first experimental observation of temporal incoherently coupled dark-bright solitons in single-mod… ▽ More We report experimental observation of incoherently coupled dark-bright vector solitons in single-mode fibers. Properties of the vector solitons agree well with those predicted by the respective systems of incoherently coupled nonlinear Schroedinger equations. To the best of our knowledge, this is the first experimental observation of temporal incoherently coupled dark-bright solitons in single-mode fibers. △ Less

Submitted 6 June, 2019; originally announced June 2019.

Comments: to be published in Optics Express

From Dye Sensitized to Perovskite Solar Cells, The Missing Link

Authors: So-Min Yoo, Seog Joon Yoon, Juan A. Anta, Hyo Joong Lee, Pablo P. Boix, Iván Mora-Seró

Abstract: Fundamental working mechanisms of perovskite solar cells remain an elusive topic of research. Impedance Spectroscopy (IS) application to perovskite-based devices generates uncommon features and misleading outputs, mainly due to the lack of a stablished model for the interpretation of the results. In this work we control the perovskite precursor concentration to fabricate a series of perovskite-bas… ▽ More Fundamental working mechanisms of perovskite solar cells remain an elusive topic of research. Impedance Spectroscopy (IS) application to perovskite-based devices generates uncommon features and misleading outputs, mainly due to the lack of a stablished model for the interpretation of the results. In this work we control the perovskite precursor concentration to fabricate a series of perovskite-based solar cells with different amounts of perovskite absorber. Low concentration devices present the well-known dye sensitized solar cell (DSSCs) impedance pattern. As the amount of perovskite is increased, the characteristic impedance spectra of thin-film perovskite solar cells (PSCs) arises. This transition is characterized by a change in the working principles, determined by an evolution of the dominant capacitance: from the intermediate frequency chemical capacitance of TiO2 in devices with isolated perovskite domains, to a large low-frequency capacitance signal which divides the spectra in two sections, yet with no direct influence in final device performance. This study allows to link experimentally, in terms of impedance behavior, PSCs with the rest of solar cell devices via DSSCs. We observe that it is not possible to assign a single physical origin to the different resistances determined in the impedance spectra except for the series resistance. In contrast, resistive element present contributions from different physical processes, observing a transport-recombination coupling. Based on this analysis we provide an equivalent circuit model to evaluate the impedance pattern of PSCs in terms of the processes directly affecting the final performance (i.e. considering transport-related and recombination-related losses), a crucial tool for further development of perovskite photovoltaics. △ Less

Submitted 22 June, 2019; v1 submitted 28 May, 2019; originally announced May 2019.

Journal ref: Joule, 2019, 3, DOI: 10.1016/j.joule.2019.07.014

Ferroelectric polarization rotation in order-disorder-type LiNbO3 thin films

Authors: Tae Sup Yoo, Sang A Lee, Changjae Roh, Seunghun Kang, Daehee Seol, Xinwei Guan, Jong-Seong Bae, Jiwoong Kim, Young-Min Kim, Hu Young Jeong, Seunggyo Jeong, Ahmed Yousef Mohamed, Deok-Yong Cho, Ji Young Jo, Sungkyun Park, Tom Wu, Yunseok Kim, Jongseok Lee, Woo Seok Choi

Abstract: The direction of ferroelectric polarization is prescribed by the symmetry of the crystal structure. Therefore, rotation of the polarization direction is largely limited, despite the opportunity it offers in understanding important dielectric phenomena such as piezoelectric response near the morphotropic phase boundaries and practical applications such as ferroelectric memory. In this study, we rep… ▽ More The direction of ferroelectric polarization is prescribed by the symmetry of the crystal structure. Therefore, rotation of the polarization direction is largely limited, despite the opportunity it offers in understanding important dielectric phenomena such as piezoelectric response near the morphotropic phase boundaries and practical applications such as ferroelectric memory. In this study, we report the observation of continuous rotation of ferroelectric polarization in order-disorder type LiNbO3 thin films. The spontaneous polarization could be tilted from an out-of-plane to an in-plane direction in the thin film by controlling the Li vacancy concentration within the hexagonal lattice framework. Partial inclusion of monoclinic-like phase is attributed to the breaking of macroscopic inversion symmetry along different directions and the emergence of ferroelectric polarization along the in-plane direction. △ Less

Submitted 7 December, 2018; originally announced December 2018.

Comments: 38 pages, 12 figures

Computational multifocal microscopy

Authors: Kuan He, Zihao Wang, Xiang Huang, Xiaolei Wang, Seunghwan Yoo, Pablo Ruiz, Itay Gdor, Alan Selewa, Nicola J. Ferrier, Norbert Scherer, Mark Hereld, Aggelos K. Katsaggelos, Oliver Cossairt

Abstract: Despite recent advances, high performance single-shot 3D microscopy remains an elusive task. By introducing designed diffractive optical elements (DOEs), one is capable of converting a microscope into a 3D "kaleidoscope", in which case the snapshot image consists of an array of tiles and each tile focuses on different depths. However, the acquired multifocal microscopic (MFM) image suffers from mu… ▽ More Despite recent advances, high performance single-shot 3D microscopy remains an elusive task. By introducing designed diffractive optical elements (DOEs), one is capable of converting a microscope into a 3D "kaleidoscope", in which case the snapshot image consists of an array of tiles and each tile focuses on different depths. However, the acquired multifocal microscopic (MFM) image suffers from multiple sources of degradation, which prevents MFM from further applications. We propose a unifying computational framework which simplifies the imaging system and achieves 3D reconstruction via computation. Our optical configuration omits chromatic correction grating and redesigns the multifocal grating to enlarge the tracking area. Our proposed setup features only one single grating in addition to a regular microscope. The aberration correction, along with Poisson and background denoising, are incorporated in our deconvolution-based fully-automated algorithm, which requires no empirical parameter-tuning. In experiments, we achieve the spatial resolutions of $0.35$um (lateral) and $0.5$um (axial), which are comparable to the resolution that can be achieved with confocal deconvolution microscopy. We demonstrate a 3D video of moving bacteria recorded at $25$ frames per second using our proposed computational multifocal microscopy technique. △ Less

Submitted 4 September, 2018; originally announced September 2018.

Comments: first appearance on Arxiv, submitted to OSA BOE

Particle Tracking and Extended Object Imaging by Interferometric Super Resolution Microscopy

Authors: Itay Gdor, Seunghwan Yoo, Xiaolei Wang, Matthew Daddysman, Rosemarie Wilton, Nicola Ferrier, Mark Hereld, Oliver, Cossairt, Aggelos Katsaggelos, Norbert F. Scherer

Abstract: An interferometric fluorescent microscope and a novel theoretic image reconstruction approach were developed and used to obtain super-resolution images of live biological samples and to enable dynamic real time tracking. The tracking utilizes the information stored in the interference pattern of both the illuminating incoherent light and the emitted light. By periodically shifting the interferomet… ▽ More An interferometric fluorescent microscope and a novel theoretic image reconstruction approach were developed and used to obtain super-resolution images of live biological samples and to enable dynamic real time tracking. The tracking utilizes the information stored in the interference pattern of both the illuminating incoherent light and the emitted light. By periodically shifting the interferometer phase and a phase retrieval algorithm we obtain information that allow localization with sub-2 nm axial resolution at 5 Hz. △ Less

Submitted 14 February, 2018; originally announced February 2018.

3D Snapshot Microscopy of Extended Objects

Authors: Xiang Huang, Alan Selewa, Xiaolei Wang, Matthew K. Daddysman, Itay Gdor, Rosemarie Wilton, Kenneth M. Kemner, Seunghwan Yoo, Aggelos K. Katsaggelos, Kuan He, Oliver Cossairt, Nicola J. Ferrier, Mark Hereld, Norbert F. Scherer

Abstract: Volumetric biological imaging often involves compromising high temporal resolution at the expense of high spatial resolution when popular scanning methods are used to capture 3D information. We introduce an integrated experimental and image reconstruction method for capturing dynamic 3D fluorescent extended objects as a series of synchronously measured 3D snapshots taken at the frame rate of the i… ▽ More Volumetric biological imaging often involves compromising high temporal resolution at the expense of high spatial resolution when popular scanning methods are used to capture 3D information. We introduce an integrated experimental and image reconstruction method for capturing dynamic 3D fluorescent extended objects as a series of synchronously measured 3D snapshots taken at the frame rate of the imaging camera. We employ multifocal microscopy (MFM) to simultaneously image at 25 focal planes and process this depth-encoded image to recover the 3D structure of extended objects, such as bacteria, using a sparsity-based reconstruction approach. The combined experimental and computational method produces image quality similar to confocal microscopy in a fraction of the acquisition time. In addition, our computational image reconstruction approach allows a simplified MFM optical design by correcting aberrations using the measured response to point sources. This "compressive" MFM acquisition and reconstruction method, where an image volume with roughly 8 million voxels is recovered from a single 1-megapixel captured image, enables straightforward study of dynamic processes in 3D, and as a simultaneous snapshot advances the state of the art in dynamic 3D microscopy. △ Less

Submitted 22 May, 2018; v1 submitted 5 February, 2018; originally announced February 2018.

Comments: 14 pages, 7 figures, 1 movie, for submission to Scientific Reports; added author and updated contributions

Exact electrodynamics versus standard optics for a slab of cold dense gas

Authors: Juha Javanainen, Janne Ruostekoski, Yi Li, Sung-Mi Yoo

Abstract: We study light propagation through a slab of cold gas using both the standard electrodynamics of polarizable media, and massive atom-by-atom simulations of the electrodynamics. The main finding is that the predictions from the two methods may differ qualitatively when the density of the atomic sample $ρ$ and the wavenumber of resonant light $k$ satisfy $ρk^{-3}\gtrsim 1$. The reason is that the st… ▽ More We study light propagation through a slab of cold gas using both the standard electrodynamics of polarizable media, and massive atom-by-atom simulations of the electrodynamics. The main finding is that the predictions from the two methods may differ qualitatively when the density of the atomic sample $ρ$ and the wavenumber of resonant light $k$ satisfy $ρk^{-3}\gtrsim 1$. The reason is that the standard electrodynamics is a mean-field theory, whereas for sufficiently strong light-mediated dipole-dipole interactions the atomic sample becomes correlated. The deviations from mean-field theory appear to scale with the parameter $ρk^{-3}$, and we demonstrate noticeable effects already at $ρk^{-3} \simeq 10^{-2}$. In dilute gases and in gases with an added inhomogeneous broadening the simulations show shifts of the resonance lines in qualitative agreement with the predicted Lorentz-Lorenz shift and "cooperative Lamb shift", but the quantitative agreement is unsatisfactory. Our interpretation is that the microscopic basis for the local-field corrections in electrodynamics is not fully understood. △ Less

Submitted 22 March, 2017; originally announced March 2017.

Journal ref: Phys. Rev. A 96, 033835 (2017)

Generalized Plasma Skimming Model for Cells and Drug Carriers in the Microvasculature

Authors: Tae-Rin Lee, Sung Sic Yoo, Jiho Yang

Abstract: In microvascular transport, where both blood and drug carriers are involved, plasma skimming has a key role on changing hematocrit level and drug carrier concentration in capillary beds after continuous vessel bifurcation in the microvasculature. While there have been numerous studies on modeling the plasma skimming of blood, previous works lacked in consideration of its interaction with drug carr… ▽ More In microvascular transport, where both blood and drug carriers are involved, plasma skimming has a key role on changing hematocrit level and drug carrier concentration in capillary beds after continuous vessel bifurcation in the microvasculature. While there have been numerous studies on modeling the plasma skimming of blood, previous works lacked in consideration of its interaction with drug carriers. In this paper, a generalized plasma skimming model is suggested to predict the redistributions of both the cells and drug carriers at each bifurcation. In order to examine its applicability, this new model was applied on a single bifurcation system to predict the redistribution of red blood cells and drug carriers. Furthermore, this model was tested at microvascular network level under different plasma skimming conditions for predicting the concentration of drug carriers. Based on these results, the applicability of this generalized plasma skimming model is fully discussed and future works along with the model's limitations are summarized. △ Less

Submitted 20 September, 2016; v1 submitted 16 September, 2016; originally announced September 2016.

Modeling the Biophysical Effects in a Carbon Beam Delivery Line using Monte Carlo Simulation

Authors: Ilsung Cho, Seung Hoon Yoo, Sungho Cho, Eun Ho Kim, Yongkeun Song, Jae-ik Shin, Won-Gyun Jung

Abstract: Relative biological effectiveness (RBE) plays an important role in designing a uniform dose response for ion beam therapy. In this study the biological effectiveness of a carbon ion beam delivery system was investigated using Monte Carlo simulation. A carbon ion beam delivery line was designed for the Korea Heavy Ion Medical Accelerator (KHIMA) project. The GEANT4 simulation tool kit was used to s… ▽ More Relative biological effectiveness (RBE) plays an important role in designing a uniform dose response for ion beam therapy. In this study the biological effectiveness of a carbon ion beam delivery system was investigated using Monte Carlo simulation. A carbon ion beam delivery line was designed for the Korea Heavy Ion Medical Accelerator (KHIMA) project. The GEANT4 simulation tool kit was used to simulate carbon beam transporting into media. An incident energy carbon ion beam in the range between 220 MeV/u and 290 MeV/u was chosen to generate secondary particles. The microdosimetric-kinetic (MK) model is applied to describe the RBE of 10% survival in human salivary gland (HSG) cells. The RBE weighted dose was estimated as a function of the penetrating depth of the water phantom along the incident beam direction. A biologically photon-equivalent Spread Out Bragg Peak (SOBP) was designed using the RBE weighted absorbed dose. Finally, the RBE of mixed beams was predicted as a function of the water phantom depth. △ Less

Submitted 21 July, 2016; originally announced July 2016.

Simulation study of dose enhancement in a cell due to nearby carbon and oxygen in particle radiotherapy

Authors: Jae Ik Shin, Ilsung Cho, Sungho Cho, Eun Ho Kim, Yongkeun Song, Won-Gyun Jung, SeungHoon Yoo, Dongho Shin, Se Byeong Lee, Myonggeun Yoon, Sebastian Incerti, Moshi Geso, Anatoly B. Rosenfeld

Abstract: The aim of this study is to investigate the dose-deposition enhancement by alpha-particle irradiation in a cellular model using carbon and oxygen chemical compositions.A simulation study was performed to study dose enhancement due to carbon and oxygen for a human cell where Geant4 code used for the alpha-particle irradiation to the cellular phantom. The characteristic of dose enhancement in the nu… ▽ More The aim of this study is to investigate the dose-deposition enhancement by alpha-particle irradiation in a cellular model using carbon and oxygen chemical compositions.A simulation study was performed to study dose enhancement due to carbon and oxygen for a human cell where Geant4 code used for the alpha-particle irradiation to the cellular phantom. The characteristic of dose enhancement in the nucleus and cytoplasm by the alpha-particle radiation was investigated based on concentrations of the carbon and oxygen compositions and was compared with those by gold and gadolinium.The results show that both the carbon and oxygen-induced dose enhancement was found to be more effective than those of gold and gadolinium. We found that the dose-enhancement effect was more dominant in the nucleus than in the cytoplasm if carbon or oxygen is uniformly distributed in a whole cell. In the condition that the added chemical composition was inserted only into the cytoplasm, the effect of the dose enhancement in nucleus becomes weak.We showed that high-stopping-power materials offer a more effective dose-enhancement efficacy and suggest that the carbon nanotubes and oxygenation are promising candidates for dose utilization as dose enhancement tools in particle therapy. △ Less

Submitted 12 March, 2015; originally announced March 2015.

Comments: 19 pages, 6 figures, 4 tables. presented to 7th KOREA-JAPAN Joint Meeting on Medical Physics (2014.09.25) accepted to Journal of the Korean Physical Society (2015.03.10)

A comparison study of ridge filter parameter using FLUKA and GEANT4 simulation code

Authors: Yongkeun Song, Jaeik Shin, Sungho Cho, Seunghoon Yoo, Ilsung Cho, Eunho Kim, Sanghyoun Choi, Kyungmin Oh, Wongyun Jung

Abstract: We investigated the parameter optimization of ridge filter thickness using a Monte Carlo simulation for carbon ion therapy. For this study, a ridge filter was designed for the Spread-Out Bragg Peak (SOBP) by considering the relative biological effect (RBE). The thickness, height, and width of the ridge filter were designed by using the FLUKA and GEANT4 code, and we analyzed and compared the result… ▽ More We investigated the parameter optimization of ridge filter thickness using a Monte Carlo simulation for carbon ion therapy. For this study, a ridge filter was designed for the Spread-Out Bragg Peak (SOBP) by considering the relative biological effect (RBE). The thickness, height, and width of the ridge filter were designed by using the FLUKA and GEANT4 code, and we analyzed and compared the results of the physical dose distribution for the FLUKA and GEANT4 coding. The results show that the minimum width of the groove for the ridge filter should be at least 0.4cm for the appropriate biological dose. The SOBP sections are 8cm, 9cm, and 10cm, respectively, when heights are 3.5cm, 4.0cm, and 4.5cm. The height of the ridge filter is designed to be associated with the SOBP width. Also, the results for the FLUKA and GEANT4 code show that an average value of difference is 3% and a maximum error is 5%; however, its trend was similar. Therefore, the height and width of the groove for the ridge filter are used for important parameters to decide the length and plateau of SOBP. △ Less

Submitted 10 March, 2015; originally announced March 2015.

Comments: 13 pages, 7 figures

Shifts of a resonance line in a dense atomic sample

Authors: Juha Javanainen, Janne Ruostekoski, Yi Li, Sung-Mi Yoo

Abstract: We study the collective response of a dense atomic sample to light essentially exactly using classical-electrodynamics simulations. In a homogeneously broadened atomic sample there is no overt Lorentz-Lorenz local field shift of the resonance, nor a collective Lamb shift. However, addition of inhomogeneous broadening restores the usual mean-field phenomenology. We study the collective response of a dense atomic sample to light essentially exactly using classical-electrodynamics simulations. In a homogeneously broadened atomic sample there is no overt Lorentz-Lorenz local field shift of the resonance, nor a collective Lamb shift. However, addition of inhomogeneous broadening restores the usual mean-field phenomenology. △ Less

Submitted 28 August, 2013; originally announced August 2013.

Journal ref: Phys. Rev. Lett 112, 113603 (2014)

Role of Energy-Level Mismatches in a Multi-Pathway Complex of Photosynthesis

Authors: James Lim, Junghee Ryu, Changhyoup Lee, Seokwon Yoo, Hyunseok Jeong, Jinhyoung Lee

Abstract: Considering a multi-pathway structure in a light-harvesting complex of photosynthesis, we investigate the role of energy-level mismatches between antenna molecules in transferring the absorbed energy to a reaction center. We find a condition in which the antenna molecules faithfully play their roles: Their effective absorption ratios are larger than those of the receiver molecule directly coupled… ▽ More Considering a multi-pathway structure in a light-harvesting complex of photosynthesis, we investigate the role of energy-level mismatches between antenna molecules in transferring the absorbed energy to a reaction center. We find a condition in which the antenna molecules faithfully play their roles: Their effective absorption ratios are larger than those of the receiver molecule directly coupled to the reaction center. In the absence of energy-level mismatches and dephasing noise, there arises quantum destructive interference between multiple paths that restricts the energy transfer. On the other hand, the destructive interference diminishes as asymmetrically biasing the energy-level mismatches and/or introducing quantum noise of dephasing for the antenna molecules, so that the transfer efficiency is greatly enhanced to near unity. Remarkably, the near-unity efficiency can be achieved at a wide range of asymmetric energy-level mismatches. Temporal characteristics are also optimized at the energy-level mismatches where the transfer efficiency is near unity. We discuss these effects, in particular, for the Fenna-Matthews-Olson complex. △ Less

Submitted 16 February, 2013; originally announced February 2013.

Journal ref: New J. Phys. 13, 103002 (2011)