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Topological Surface State Evolution in Bi2Se3 via Surface Etching

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arXiv 2024年

作者： Yue, Ziqin Huang, Jianwei Wang, Ruohan Li, Jia-Wan Rong, Hongtao Guo, Yucheng Wu, Han Zhang, Yichen Kono, Junichiro Zhou, Xingjiang Hou, Yusheng Wu, Ruqian Yi, Ming Department of Physics and Astronomy Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices Center for Neutron Science and Technology School of Physics Sun Yat-Sen University Guangzhou510275 China Institute of Physics Chinese Academy of Sciences Beijing100190 China Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States Department of Physics and Astronomy University of California Irvine IrvineCA92697 United States

Topological insulators are materials with an insulating bulk interior while maintaining gapless boundary states against back scattering. Bi2Se3 is a prototypical topological insulator with a Dirac-cone surface state around Γ. Here, we present a controlled methodology to gradually remove Se atoms from the surface Se-Bi-Se-Bi-Se quintuple layers, eventually forming bilayer-Bi on top of the quintuple bulk. Our method allows us to track the topological surface state and confirm its robustness throughout the surface modification. Importantly, we report a relocation of the topological Dirac cone in both real space and momentum space, as the top surface layer transitions from quintuple Se-Bi-Se-Bi-Se to bilayer-Bi. Additionally, charge transfer among different surface layers is identified. Our study provides a precise method to manipulate surface configurations, allowing for the fine-tuning of the topological surface states in Bi2Se3, which represents a significant advancement towards nano-engineering of topological states. Copyright © 2024, The Authors. All rights reserved.

关键词： Topological insulators

Transfer learning approach to modeling multichannel gate-all-around nanosheet field-effect transistors under work function fluctuations

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engineering Applications of Artificial Intelligence 2025年 148卷

作者： Dash, Sagarika Li, Yiming Parallel and Scientific Computing Laboratory National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Communications Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Biomedical Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Artificial Intelligence Innovation National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Department of Electronics and Electrical Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Department of Microelectronics National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan

Deep learning (DL) has significantly advanced various industries, including semiconductors, by providing sophisticated methods for analyzing emerging device data. Transfer learning (TL), a prominent DL topology, leverages knowledge from a pre-trained model to improve the performance of a specific target task. This study aims to apply TL to predict the impact of work function fluctuation (WKF) on the variability of threshold voltage (σVTH) in gate-all-around silicon nanosheet field-effect transistors (GAA Si NS FETs). We used TL to transfer knowledge from models trained on 1-channel (1ch) GAA Si NS FETs to more complex 4-channel (4ch) counterparts. The prediction accuracy is evaluated by using the mean square error(MSE), root mean square error (RMSE), and the mean absolute error(MAE) metrics. Our TL approach achieved an error rate of less than 1%, demonstrating high accuracy in predicting WKF effects on σ VTH of 4ch GAA Si NS FETs using only 500 samples. This study presents a pioneering application of TL using a one-dimensional (1D) convolutional neural network (CNN) long short-term memory (LSTM) topology in semiconductor device technology. The results effectively address the challenge of predicting σ VTH variations in advanced GAA Si NS FETs because of WKF, which eliminates the need for extensive semiconductor device simulations. © 2025 Elsevier Ltd

关键词： Convolutional neural networks

Competing epidemics on graphs-global convergence and coexistence

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arXiv 2021年

作者： Doshi, Vishwaraj Mallick, Shailaja Eun, Do Young Operations Research Graduate Program North Carolina State University RaleighNC United States Department of Computer Science North Carolina State University RaleighNC United States Department of Electrical and Computer Engineering North Carolina State University RaleighNC United States

The dynamics of the spread of contagions such as viruses, infectious diseases or even rumors/opinions over contact networks (graphs) have effectively been captured by the well known Susceptible-Infected-Susceptible (SIS) epidemic model in recent years. When it comes to competition between two such contagions spreading on overlaid graphs, their propagation is captured by so-called bi-virus epidemic models. Analysis of such dynamical systems involve the identification of equilibrium points and its convergence properties, which determine whether either of the viruses dies out, or both survive together. We demonstrate how the existing works are unsuccessful in characterizing a large subset of the model parameter space, including all parameters for which the competitiveness of the bi-virus system is significant enough to attain coexistence of the epidemics. In this paper, we fill in this void and obtain convergence results for the entirety of the model parameter space;giving precise conditions (necessary and sufficient) under which the system globally converges to a trichotomy of possible outcomes: a virus-free state, a single-virus state, and to a coexistence state-the first such result. Copyright © 2021, The Authors. All rights reserved.

关键词： Viruses

Observation of Temporal Reflections and Broadband Frequency Translations at Photonic Time-Interfaces

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arXiv 2022年

作者： Moussa, Hady Xu, Gengyu Yin, Shixiong Galiffi, Emanuele Ra'di, Younes Alù, Andrea Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Department of Electric Engineering City College of City University of New York New YorkNY10031 United States Department of Electrical Engineering and Computer Science Syracuse University SyracuseNY13244 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States

Time-reflection is a uniform inversion of the temporal evolution of a signal, which arises when an abrupt change in the properties of the host material occurs uniformly in space.1-3 At such a time-interface, a portion of the input signal is time-reversed, and its frequency spectrum is homogeneously translated while its momentum is conserved, forming the temporal counterpart of a spatial interface. Combinations of time-interfaces, forming time-metamaterials and Floquet matter4, exploit the interference of multiple time-reflections for extreme wave manipulation, leveraging time as a new degree of freedom5. Here, we report the observation of photonic time-reflection and associated broadband frequency translation in a switched transmission-line metamaterial whose effective capacitance is homogeneously and abruptly changed via a synchronized array of switches. A pair of temporal interfaces are combined to demonstrate time-reflection-induced wave interference, realizing the temporal counterpart of a Fabry-Perot cavity6. Our results establish the foundational building blocks to realize time-metamaterials and Floquet photonic crystals, with opportunities for extreme photon manipulation in space and time7-8 © 2022, CC BY.

关键词： Metamaterials

Data Processing of Functional Optical Microscopy for Neuroscience

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arXiv 2022年

作者： Benisty, Hadas Song, Alexander Mishne, Gal Charles, Adam S. Yale Neuroscience New HavenCT06510 United States Max Planck Institute for Intelligent Systems Stuttgart Germany Halıcıoğlu Data Science Institute Department of Electrical and Computer Engineering Neurosciences Graduate Program UC San Diego 9500 Gilman Drive La Jolla CA92093 United States Department of Biomedical Engineering Kavli Neuroscience Discovery Institute Center for Imaging Science Department of Neuroscience Mathematical Institute for Data Science Johns Hopkins University BaltimoreMD21287 United States

Functional optical imaging in neuroscience is rapidly growing with the development of new optical systems and fluorescence indicators. To realize the potential of these massive spatiotemporal datasets for relating neuronal activity to behavior and stimuli and uncovering local circuits in the brain, accurate automated processing is increasingly essential. In this review, we cover recent computational developments in the full data processing pipeline of functional optical microscopy for neuroscience data and discuss ongoing and emerging challenges. © 2022, CC BY-NC-ND.

关键词： Fluorescence microscopy

Internet of Things in disaster management systems: A systematic review

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AIP Conference Proceedings 2023年第1期2594卷

作者： G. P. L. Barnabas H. Citrawati T. A. Santoso N. Surantha Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Department of Electrical Electronic and Communication Engineering Faculty of Engineering Tokyo City University Tamazutsumi 1-28-1 Setagaya City Tokyo 158-8557 Japan

Disasters are unexpected, sudden events that may cause monumental damages to both materials and human lives. Hence, the unpredictability and destruction that follow when a disaster happens to pose a dire challenge for researchers, scientists, and governments to find ways to effectively handle the situation and flow of information when such an event strikes. The Internet of Things (IoT) presents a solution that involves using one or more mobile devices connected over the network, thus making the transfer of data and information more effective and efficient. This paper presents a holistic analysis of the state-of-the-art IoT technologies which can answer the challenge posed above, in addition to the demand for efficiency, mobility, accuracy, as well as better communications between the command center and all actors involved in disaster operations. The applications of IoT have varied for many purposes. We divide those purposes into two broad strokes of pre-disaster management and post-disaster management IoT-aided systems. For pre-disaster management, the IoT-aided systems are mostly used to predict the possible disaster that might happen using sensors placed in strategic places. Finally, for post-disaster management, the IoT-aided systems aid in improving the effectiveness and accuracy of evacuation, relief, and victim-tracking efforts.

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A universal optical modulator for synthetic topologically tuneable structured matter

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arXiv 2023年

作者： He, Chao Chen, Binguo Song, Zipei Zhao, Zimo Ma, Yifei He, Honghui Luo, Lin Marozsak, Tade Wang, An Xu, Rui Huang, Peixiang Qiu, Xuke Sun, Bangshan Cui, Jiahe Cai, Yuxi Zhang, Yun Salter, Patrick Fells, Julian A.J. Dai, Ben Liu, Shaoxiong Guo, Limei Ma, Hui Elston, Steve J. Zhan, Qiwen Qiu, Chengwei Morris, Stephen M. Booth, Martin J. Forbes, Andrew Department of Engineering Science University of Oxford Parks Road OxfordOX1 3PJ United Kingdom Guangdong Research Center of Polarization Imaging and Measurement Engineering Technology Institute of Biopharmaceutical and Health Engineering Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China College of Engineering Peking University Beijing100871 China School of Archaeology University of Oxford South Parks Road OxfordOX1 3TG United Kingdom Institute of Archaeology Chinese Academy of Social Sciences Beijing100101 China Department of Statistics The Chinese University of Hong Kong Shatin Hong Kong Shenzhen Sixth People’s Hospital Huazhong University of Science and Technology Union Shenzhen Hospital Shenzhen518052 China Department of Pathology School of Basic Medical Science Peking University Health Science Center Peking University Third Hospital Beijing China School of Optical‑Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore School of Physics University of the Witwatersrand Private Bag 3 Johannesburg2050 South Africa

Topologically structured matter, such as metasurfaces and metamaterials, have given rise to impressive photonic functionality, fuelling diverse applications from microscopy and holography to encryption and communication. Presently these solutions are limited by their largely static nature and preset functionality, hindering applications that demand dynamic photonic systems with reconfigurable topologies. Here we demonstrate a universal optical modulator that implements topologically tuneable structured matter as virtual pixels derived from cascading low functionality tuneable devices, altering the paradigm of phase and amplitude control to encompass arbitrary spatially varying retarders in a synthetic structured matter device. Our approach opens unprecedented functionality that is user-defined with high flexibility, allowing our synthetic structured matter to act as an information carrier, beam generator, analyser, and corrector, opening an exciting path to tuneable topologies of light and matter. Copyright © 2023, The Authors. All rights reserved.

关键词： Light modulators

Self-supervised approach for facial movement based optical flow

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arXiv 2021年

作者： Alkaddour, Muhannad Tariq, Usman Dhall, Abhinav Graduate Program in Mechatronics Engineering American University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering American University of Sharjah Sharjah United Arab Emirates Department of Human Centred Computing Monash University Melbourne Australia Department of Computer Science and Engineering Indian Institute of Technology Ropar Rupnagar India

Computing optical flow is a fundamental problem in computer vision. However, deep learning-based optical flow techniques do not perform well for non-rigid movements such as those found in faces, primarily due to lack of the training data representing the fine facial motion. We hypothesize that learning optical flow on face motion data will improve the quality of predicted flow on faces. The aim of this work is threefold: (1) exploring self-supervised techniques to generate optical flow ground truth for face images;(2) computing baseline results on the effects of using face data to train Convolutional Neural Networks (CNN) for predicting optical flow;and (3) using the learned optical flow in micro-expression recognition to demonstrate its effectiveness. We generate optical flow ground truth using facial key-points in the BP4D-Spontaneous dataset. The generated optical flow is used to train the FlowNetS architecture to test its performance on the generated dataset. The performance of FlowNetS trained on face images surpassed that of other optical flow CNN architectures, demonstrating its usefulness. Our optical flow features are further compared with other methods using the STSTNet micro-expression classifier, and the results indicate that the optical flow obtained using this work has promising applications in facial expression analysis. © 2021, CC BY.

关键词： Optical flows

cytoNet: Spatiotemporal network analysis of cell communities (vol 18, e1009846, 2022)

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PLOS COMPUTATIONAL BIOLOGY 2022年第11期18卷 e1009846页

作者： Mahadevan, A. S. Long, B. L. Hu, C. W. Ryan, D. T. Grandel, N. E. Department of Bioengineering University of Pennsylvania Philadelphia Pennsylvania United States of America. Department of Bioengineering Rice University Houston Texas United States of America. Systems Synthetic and Physical Biology Program Rice University Houston Texas United States of America. Department of Biomedical Engineering University of Texas at San Antonio San Antonio Texas United States of America. Biophysics Graduate Program University of California Berkeley California United States of America. Department of Oral & Maxillofacial Surgery University of Texas Health Science Center at San Antonio San Antonio Texas United States of America. Department of Electrical and Computer Engineering Rice University Houston Texas United States of America.

We introduce cytoNet, a cloud-based tool to characterize cell populations from microscopy images. cytoNet quantifies spatial topology and functional relationships in cell communities using principles of network science. Capturing multicellular dynamics through graph features, cytoNet also evaluates the effect of cell-cell interactions on individual cell phenotypes. We demonstrate cytoNet's capabilities in four case studies: 1) characterizing the temporal dynamics of neural progenitor cell communities during neural differentiation, 2) identifying communities of pain-sensing neurons in vivo, 3) capturing the effect of cell community on endothelial cell morphology, and 4) investigating the effect of laminin α4 on perivascular niches in adipose tissue. The analytical framework introduced here can be used to study the dynamics of complex cell communities in a quantitative manner, leading to a deeper understanding of environmental effects on cellular behavior. The versatile, cloud-based format of cytoNet makes the image analysis framework accessible to researchers across domains.

关键词： Calcium imaging Fluorescence imaging Growth factors Centrality Cell cycle and cell division Endothelial cells Network analysis Stem cells