Showing 1–11 of 11 results for author: Kwon, M

Converting coherence into work with a fully quantum engine

Authors: MinSik Kwon, Tobias Denzler, Rouven Maier, Vadim Vorobyov, Durga Bhaktavatsala Rao Dasari, Eric Lutz, Jörg Wrachtrup

Abstract: Heat engines convert thermal energy into mechanical work. We here report the experimental realization of a fully quantum engine that converts quantum coherence into work. A single solid-state spin in diamond is fueled by a coherent bath and cyclically stores energy in a spin quantum battery. We establish quantum-enhanced performance by showing that almost 200$\%$ more work is produced after a few… ▽ More Heat engines convert thermal energy into mechanical work. We here report the experimental realization of a fully quantum engine that converts quantum coherence into work. A single solid-state spin in diamond is fueled by a coherent bath and cyclically stores energy in a spin quantum battery. We establish quantum-enhanced performance by showing that almost 200$\%$ more work is produced after a few cycle compared to the corresponding classical engine. We obtain concrete criteria for successful coherence-to-work conversion, and highlight the importance of a coherent motor-battery interaction. This device harnesses nonclassical features during all stages of its cycle, and demonstrates the functionality of a nanomachine whose parts are all quantum coherent. △ Less

Submitted 8 November, 2025; originally announced November 2025.

Comments: 9 pages, 3 figures

A Computational Study for Screening High-Selectivity Inhibitors in Area-Selective Atomic Layer Deposition on Amorphous Surfaces

Authors: Gijin Kim, Purun-hanul Kim, Suk Gyu Hahm, Myongjong Kwon, Byungha Park, Changho Hong, Seungwu Han

Abstract: Area-selective atomic layer deposition (AS-ALD) is an emerging technology in semiconductor manufacturing. However, accurately understanding inhibitor reactivity on surfaces remains challenging, particularly when the substrate is amorphous. In this study, we employ density functional theory (DFT) to investigate reaction pathways and quantify the reactivity of (N,N-dimethylamino)trimethylsilane (DMA… ▽ More Area-selective atomic layer deposition (AS-ALD) is an emerging technology in semiconductor manufacturing. However, accurately understanding inhibitor reactivity on surfaces remains challenging, particularly when the substrate is amorphous. In this study, we employ density functional theory (DFT) to investigate reaction pathways and quantify the reactivity of (N,N-dimethylamino)trimethylsilane (DMATMS) and ethyltrichlorosilane (ETS) at silanol (-OH), siloxane (-O-), amine (-NH2), and imide (-NH-) sites on both amorphous and crystalline silicon oxide and silicon nitride surfaces. Notably, both molecules exhibit greater reactivity toward terminal sites (-OH and -NH2) on amorphous surfaces compared to crystalline counterparts. For bridge sites, -O- and -NH-, multiple reaction pathways are identified, with bridge-cleavage reactions being the predominant mechanism, except for DMATMS reactions with nitride surfaces. The reactivity of DMATMS with -NH- sites is comparable to that with -NH2, with both reactions yielding volatile products. This study underscores the importance of amorphous surface modeling in reliably predicting inhibitor adsorption and reactivity on realistic surfaces. Moreover, we outline a computational screening approach that accounts for site-specific precursor-inhibitor interactions, enabling efficient and rational theoretical design of AS-ALD precursor-inhibitor pairs. △ Less

Submitted 20 October, 2025; originally announced October 2025.

Comments: 27 pages, 5 figures, 1 table. Supplementary information included as ancillary file (+9 pages)

Re-experiment Smart: a Novel Method to Enhance Data-driven Prediction of Mechanical Properties of Epoxy Polymers

Authors: Wanshan Cui, Yejin Jeong, Inwook Song, Gyuri Kim, Minsang Kwon, Donghun Lee

Abstract: Accurate prediction of polymer material properties through data-driven approaches greatly accelerates novel material development by reducing redundant experiments and trial-and-error processes. However, inevitable outliers in empirical measurements can severely skew machine learning results, leading to erroneous prediction models and suboptimal material designs. To address this limitation, we prop… ▽ More Accurate prediction of polymer material properties through data-driven approaches greatly accelerates novel material development by reducing redundant experiments and trial-and-error processes. However, inevitable outliers in empirical measurements can severely skew machine learning results, leading to erroneous prediction models and suboptimal material designs. To address this limitation, we propose a novel approach to enhance dataset quality efficiently by integrating multi-algorithm outlier detection with selective re-experimentation of unreliable outlier cases. To validate the empirical effectiveness of the approach, we systematically construct a new dataset containing 701 measurements of three key mechanical properties: glass transition temperature ($T_g$), tan $δ$ peak, and crosslinking density ($v_{c}$). To demonstrate its general applicability, we report the performance improvements across multiple machine learning models, including Elastic Net, SVR, Random Forest, and TPOT, to predict the three key properties. Our method reliably reduces prediction error (RMSE) and significantly improves accuracy with minimal additional experimental work, requiring only about 5% of the dataset to be re-measured. These findings highlight the importance of data quality enhancement in achieving reliable machine learning applications in polymer science and present a scalable strategy for improving predictive reliability in materials science. △ Less

Submitted 19 May, 2025; originally announced June 2025.

Comments: 27 pages, 8 figures

Self-patterning of Liquid Field's Metal for Enhanced Performance of Two-dimensional Semiconductor

Authors: Kwanghee Han, Heeyeon Lee, Minseong Kwon, Vinod Menon, Chaun Jang, Young Duck Kim

Abstract: Two-dimensional (2D) van der Waals semiconductors show promise for atomically thin flexible and transparent optoelectronic devices in future technologies.However, developing high-performance field-effect transistors (FETs) based on 2D materials is impeded by two key challenges, the high contact resistance at the 2D semiconductors-metal interface and the limited effective doping strategies. Here, w… ▽ More Two-dimensional (2D) van der Waals semiconductors show promise for atomically thin flexible and transparent optoelectronic devices in future technologies.However, developing high-performance field-effect transistors (FETs) based on 2D materials is impeded by two key challenges, the high contact resistance at the 2D semiconductors-metal interface and the limited effective doping strategies. Here, we present a novel approach to overcome these challenges using self-propagating liquid Fields metal, a eutectic alloy with a low melting point of approximately 62 C. By modifying pre-patterned electrodes on WSe2 FETs through the deposition of Fields metal onto contact pad edges followed by vacuum annealing, we create new semimetal electrodes that seamlessly incorporate the liquid metal into 2D semiconductors. This integration preserves the original electrode architecture while transforming to semimetal compositions of Fields metal such as Bi, In, and Sn modifies the work functions to 2D semiconductors, resulting in reduced contact resistance without inducing Fermi-level pinning and charge carrier mobilities. Our method enhances the electrical performance of 2D devices and opens new avenues for designing high-resolution liquid metal circuits suitable for stretchable, flexible, and wearable 2D semiconductor applications. △ Less

Submitted 24 May, 2025; originally announced May 2025.

Comments: 17 pages, 4 figures

Flux pinning in superconducting multilayer 2H-NbSe$_2$ nano-step junction

Authors: Minseong Kwon, Mingi Kim, Yoonji Gong, Heeyeon Lee, Young Duck Kim

Abstract: Superconductors exhibit dissipationless supercurrents even under finite bias and magnetic field conditions, provided these remain below the critical values. However, type-II superconductors in the flux flow regime display Ohmic dissipation arising from vortex dynamics under finite magnetic fields. The interplay between supercurrent and Ohmic dissipation in a type-II superconductor is dictated by v… ▽ More Superconductors exhibit dissipationless supercurrents even under finite bias and magnetic field conditions, provided these remain below the critical values. However, type-II superconductors in the flux flow regime display Ohmic dissipation arising from vortex dynamics under finite magnetic fields. The interplay between supercurrent and Ohmic dissipation in a type-II superconductor is dictated by vortex motion and the robustness of vortex pinning forces. In this study, we present an experimental investigation of the superconducting phase transitions and vortex dynamics in the atomically thin type-II superconductor 2H-NbSe$_2$. We fabricated a high-quality multilayer 2H-NbSe$_2$ with a step junction, demonstrating supercurrent in clean limit below a critical temperature of 6.6 K and a high residual resistance ratio of 17. The upper critical field was estimated to be 4.5 T and the Ginzburg-Landau coherence length 8.6 nm. Additionally, we observed phase transitions induced by vortex viscous dynamics in the 2H-NbSe$_2$ step junction. Analysis of the pinning force density using the Dew-Hughes model indicates that the pinning force in the 2H-NbSe$_2$ device can be attributed to step junction, related to the surface-$Δκ$ type of pinning centers. Our findings pave the way for engineering pinning forces by introducing artificial pinning centers through partial atomic thickness variation in layered 2D superconductors while minimizing unwanted quality degradation in the system. △ Less

Submitted 7 January, 2025; originally announced January 2025.

Comments: 19 pages, 4 figures

Journal ref: Materials for Quantum Technology 5, 015201 (2025)

Spin-orbit-splitting-driven nonlinear Hall effect in NbIrTe4

Authors: Ji-Eun Lee, Aifeng Wang, Shuzhang Chen, Minseong Kwon, Jinwoong Hwang, Minhyun Cho, Ki-Hoon Son, Dong-Soo Han, Jun Woo Choi, Young Duck Kim, Sung-Kwan Mo, Cedomir Petrovic, Choongyu Hwang, Se Young Park, Chaun Jang, Hyejin Ryu

Abstract: The Berry curvature dipole (BCD) serves as a one of the fundamental contributors to emergence of the nonlinear Hall effect (NLHE). Despite intense interest due to its potential for new technologies reaching beyond the quantum efficiency limit, the interplay between BCD and NLHE has been barely understood yet in the absence of a systematic study on the electronic band structure. Here, we report NLH… ▽ More The Berry curvature dipole (BCD) serves as a one of the fundamental contributors to emergence of the nonlinear Hall effect (NLHE). Despite intense interest due to its potential for new technologies reaching beyond the quantum efficiency limit, the interplay between BCD and NLHE has been barely understood yet in the absence of a systematic study on the electronic band structure. Here, we report NLHE realized in NbIrTe4 that persists above room temperature coupled with a sign change in the Hall conductivity at 150 K. First-principles calculations combined with angle-resolved photoemission spectroscopy (ARPES) measurements show that BCD tuned by the partial occupancy of spin-orbit split bands via temperature is responsible for the temperature-dependent NLHE. Our findings highlight the correlation between BCD and the electronic band structure, providing a viable route to create and engineer the non-trivial Hall effect by tuning the geometric properties of quasiparticles in transition-metal chalcogen compounds. △ Less

Submitted 21 August, 2024; originally announced August 2024.

Journal ref: Nature Communications 15, 3971 (2024)

Large-scale quantum reservoir learning with an analog quantum computer

Authors: Milan Kornjača, Hong-Ye Hu, Chen Zhao, Jonathan Wurtz, Phillip Weinberg, Majd Hamdan, Andrii Zhdanov, Sergio H. Cantu, Hengyun Zhou, Rodrigo Araiza Bravo, Kevin Bagnall, James I. Basham, Joseph Campo, Adam Choukri, Robert DeAngelo, Paige Frederick, David Haines, Julian Hammett, Ning Hsu, Ming-Guang Hu, Florian Huber, Paul Niklas Jepsen, Ningyuan Jia, Thomas Karolyshyn, Minho Kwon , et al. (28 additional authors not shown)

Abstract: Quantum machine learning has gained considerable attention as quantum technology advances, presenting a promising approach for efficiently learning complex data patterns. Despite this promise, most contemporary quantum methods require significant resources for variational parameter optimization and face issues with vanishing gradients, leading to experiments that are either limited in scale or lac… ▽ More Quantum machine learning has gained considerable attention as quantum technology advances, presenting a promising approach for efficiently learning complex data patterns. Despite this promise, most contemporary quantum methods require significant resources for variational parameter optimization and face issues with vanishing gradients, leading to experiments that are either limited in scale or lack potential for quantum advantage. To address this, we develop a general-purpose, gradient-free, and scalable quantum reservoir learning algorithm that harnesses the quantum dynamics of neutral-atom analog quantum computers to process data. We experimentally implement the algorithm, achieving competitive performance across various categories of machine learning tasks, including binary and multi-class classification, as well as timeseries prediction. Effective and improving learning is observed with increasing system sizes of up to 108 qubits, demonstrating the largest quantum machine learning experiment to date. We further observe comparative quantum kernel advantage in learning tasks by constructing synthetic datasets based on the geometric differences between generated quantum and classical data kernels. Our findings demonstrate the potential of utilizing classically intractable quantum correlations for effective machine learning. We expect these results to stimulate further extensions to different quantum hardware and machine learning paradigms, including early fault-tolerant hardware and generative machine learning tasks. △ Less

Submitted 2 July, 2024; originally announced July 2024.

Comments: 10 + 14 pages, 4 + 7 figures

Jet-Loaded Cold Atomic Beam Source for Strontium

Authors: Minho Kwon, Aaron Holman, Quan Gan, Chun-Wei Liu, Matthew Molinelli, Ian Stevenson, Sebastian Will

Abstract: We report on the design and characterization of a cold atom source for strontium (Sr) based on a two-dimensional magneto-optical trap (MOT) that is directly loaded from the atom jet of a dispenser. We characterize the atom flux of the source by measuring the loading rate of a three-dimensional MOT. We find loading rates of up to $10^{8}$ atoms per second. The setup is compact, easy to construct, a… ▽ More We report on the design and characterization of a cold atom source for strontium (Sr) based on a two-dimensional magneto-optical trap (MOT) that is directly loaded from the atom jet of a dispenser. We characterize the atom flux of the source by measuring the loading rate of a three-dimensional MOT. We find loading rates of up to $10^{8}$ atoms per second. The setup is compact, easy to construct, and has low power consumption. It addresses the long standing challenge of reducing the complexity of cold beam sources for Sr, which is relevant for optical atomic clocks and quantum simulation and computing devices based on ultracold Sr. △ Less

Submitted 2 February, 2023; v1 submitted 25 October, 2022; originally announced October 2022.

Comments: 6 pages, 4 figures

Metasurface Holographic Optical Traps for Ultracold Atoms

Authors: Xiaoyan Huang, Weijun Yuan, Aaron Holman, Minho Kwon, Stuart J. Masson, Ricardo Gutierrez-Jauregui, Ana Asenjo-Garcia, Sebastian Will, Nanfang Yu

Abstract: We propose metasurface holograms as a novel platform to generate optical trap arrays for cold atoms with high fidelity, efficiency, and thermal stability. We developed design and fabrication methodologies to create dielectric, phase-only metasurface holograms based on titanium dioxide. We experimentally demonstrated optical trap arrays of various geometries, including periodic and aperiodic config… ▽ More We propose metasurface holograms as a novel platform to generate optical trap arrays for cold atoms with high fidelity, efficiency, and thermal stability. We developed design and fabrication methodologies to create dielectric, phase-only metasurface holograms based on titanium dioxide. We experimentally demonstrated optical trap arrays of various geometries, including periodic and aperiodic configurations with dimensions ranging from 1D to 3D and the number of trap sites up to a few hundred. We characterized the performance of the holographic metasurfaces in terms of the positioning accuracy, size and intensity uniformity of the generated traps, and power handling capability of the dielectric metasurfaces. Our proposed platform has great potential for enabling fundamental studies of quantum many-body physics, and quantum simulation and computation tasks. The compact form factor, passive nature, good power handling capability, and scalability of generating high-quality, large-scale arrays also make the metasurface platform uniquely suitable for realizing field-deployable devices and systems based on cold atoms. △ Less

Submitted 13 October, 2022; originally announced October 2022.

Observation of a partially rotating superfluid of exciton-polariton

Authors: Daegwang Choi, Min Park, Byoung Yong Oh, Min-Sik Kwon, Suk In Park, Sooseok Kang, Jin Dong Song, Yong-Hoon Cho, Hyoungsoon Choi

Abstract: Rotation of a container holding a viscous fluid forms a vortex which grows with increasing angular velocity. A superfluid, however, is intrinsically different from these normal fluids because its rotation is quantized. Even if a container of superfluid is rotating, the fluid itself remains still until a critical velocity is reached. Beyond the critical velocity, all the particles conspire to sudde… ▽ More Rotation of a container holding a viscous fluid forms a vortex which grows with increasing angular velocity. A superfluid, however, is intrinsically different from these normal fluids because its rotation is quantized. Even if a container of superfluid is rotating, the fluid itself remains still until a critical velocity is reached. Beyond the critical velocity, all the particles conspire to suddenly pick up an angular momentum of $\hbar$ each and forms a quantized vortex. As a result, a superfluid is known to increase its rotation by a total angular momentum of $N\hbar$. In this letter, we show that exciton-polariton superfluid can split into an irrotational part and a rotational part. The relative ratio between the two states can be controlled by either pump beam's power or spot size. Consequently, angular momentum of exciton-polariton superfluid can be tuned from zero to $N\hbar$ continuously. This striking observation sets the stage for studying non-equilibrium properties of a superfluid with exciton-polaritons. △ Less

Submitted 20 October, 2020; v1 submitted 5 September, 2019; originally announced September 2019.

Comments: A follow-up study on the two-states in the exciton-polariton superfluid reported in this article revealed that the two states occur sequentially and not concurrently. The apparent coexistence of the two states, thus, is not due to partial rotation of a superfluid. This new finding alters the conclusion of this article significantly so as to necessitate a withdrawal instead of an update

Direct Transfer of Light's Orbital Angular Momentum onto Non-resonantly Excited Polariton Superfluid

Authors: Byoung Yong Oh, Min-Sik Kwon, Su-Hyun Gong, Je-Hyung Kim, Hang Kyu Kang, Sooseok Kang, Jin Dong Song, Hyoungsoon Choi, Yong-Hoon Cho

Abstract: Recently, exciton-polaritons in a semiconductor microcavity were found to condense into a coherent ground state much like a Bose-Einstein condensate and a superfluid. They have become a unique testbed for generating and manipulating quantum vortices in a driven-dissipative superfluid. Here, we generate exciton-polariton condensate with non-resonant Laguerre-Gaussian (LG) optical beam and verify th… ▽ More Recently, exciton-polaritons in a semiconductor microcavity were found to condense into a coherent ground state much like a Bose-Einstein condensate and a superfluid. They have become a unique testbed for generating and manipulating quantum vortices in a driven-dissipative superfluid. Here, we generate exciton-polariton condensate with non-resonant Laguerre-Gaussian (LG) optical beam and verify the direct transfer of light's orbital angular momentum to exciton-polariton quantum fluid. Quantized vortices are found in spite of large energy relaxation involved in non-resonant pumping. We identified phase singularity, density distribution and energy eigenstates for the vortex states. Our observations confirm that non-resonant optical LG beam can be used to manipulate chirality, topological charge, and stability of non-equilibrium quantum fluid. These vortices are quite robust, only sensitive to the OAM of light and not other parameters such as energy, intensity, size or shape of the pump beam. Therefore, optical information can be transferred between photon and exciton-polariton with ease and the technique is potentially useful to form the controllable network of multiple topological charges even in the presence of spectral randomness in solid state system. △ Less

Submitted 8 November, 2018; v1 submitted 24 August, 2017; originally announced August 2017.

Comments: Main body: 18 pages, 4 figures / Supplementary materials: 20 pages 8 figures

Journal ref: Phys. Rev. Lett. 122, 045302 (2019)