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Graphene Bridge Heterostructure Devices for Negative Differential Transconductance Circuit Applications

Nano-Micro Letters 2023年第2期15卷 161-171页

作者： Minjong Lee Tae Wook Kim Chang Yong Park Kimoon Lee Takashi Taniguchi Kenji Watanabe Min‑gu Kim Do Kyung Hwang Young Tack Lee Department of Electrical and Computer Engineering Inha UniversityIncheon 22212Republic of Korea Department of Materials Science and Engineering The University of Texas at DallasRichardsonTX 75080USA Center for Opto‑Electronic Materials and Devices Post‑Silicon Semiconductor InstituteKorea Institute of Science and Technology(KIST)Seoul 02792Republic of Korea Department of Physics Kunsan National UniversityGunsan 54150Republic of Korea Advanced Materials Laboratory National Institute for Materials ScienceTsukuba 305‑0044Japan Department of Information and Communication Engineering Inha UniversityIncheon 22212Republic of Korea Division of Nanoscience and Technology KIST SchoolUniversity of Science and Technology(UST)Seoul 02792Republic of Korea Department of Electronic Engineering Inha UniversityIncheon 22212Republic of Korea

Two-dimensional van der Waals(2D vdW)material-based heterostructure devices have been widely studied for high-end electronic applications owing to their heterojunction *** this study,we demonstrate graphene(Gr)-bridge heterostructure devices consisting of laterally series-connected ambipolar semiconductor/Gr-bridge/n-type molybdenum disulfide as a channel material for field-effect transistors(FET).Unlike conventional FET operation,our Gr-bridge devices exhibit nonclassical transfer characteristics(humped transfer curve),thus possessing a negative differential *** phenomena are interpreted as the operating behavior in two series-connected FETs,and they result from the gate-tunable contact capacity of the Gr-bridge ***-value logic inverters and frequency tripler circuits are successfully demonstrated using ambipolar semiconductors with narrow-and wide-bandgap materials as more advanced circuit applications based on non-classical transfer ***,we believe that our innovative and straightforward device structure engineering will be a promising technique for future multi-functional circuit applications of 2D nanoelectronics.

关键词： Graphene bridge Heterostructure device Non-classical transfer characteristics Multi-value logic inverter Frequency tripler

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Spatiotemporal Stacked Autoencoder based Soft Sensor Modeling for the Dow Data Challenge Problem 9

Spatiotemporal Stacked Autoencoder based Soft Sensor Modelin...

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9th IEEE Smart World Congress, SWC 2023

作者： Zhu, Xiuli Damarla, Seshu Kumar Huang, Biao University of Shanghai for Science and Technology Department of Optical-Electrical and Computer Engineering Shanghai China University of Alberta Department of Chemical and Materials Engineering AB Canada

ISBN: (纸本)9798350319804

Process data with characteristics such as strong nonlinearity, high dimensionality, cross-correlations and auto correlations pose a great challenge for data-driven soft sensor modeling. Albeit the conventional stacked Autoencoder (SAE) is able to learn low dimensional nonlinear features from the process data, it fails to extract spatial and temporal features. To overcome this shortcoming, in the present work, spatiotemporal SAE (STSAE) is proposed based on convolutional and LSTM layers. Lower dimensional nonlinear features (spatial and temporal) extracted from STSAE are used to build the soft sensor. The effectiveness of the STSAE-based soft sensor is demonstrated in an industrial case study, the Dow data challenge problem. For the purpose of comparison, soft sensor models based on popular machine learning algorithms are developed and applied to the industrial case study. The obtained results demonstrate that the proposed STSAE based soft sensor yields more accurate estimations for impurity level at the primary column outlet of Dow process refining system. © 2023 IEEE.

关键词： autoencoder CNN Dow data challenge problem LSTM stacked Autoencoder

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Spectral Learning of Shared Dynamics Between Generalized-Linear Processes 38

Spectral Learning of Shared Dynamics Between Generalized-Lin...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Oganesian, Lucine L. Sani, Omid G. Shanechi, Maryam M. Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States Ming Hsieh Department of Electrical and Computer Engineering Thomas Lord Department of Computer Science Alfred E. Mann Department of Biomedical Engineering Neuroscience Graduate Program University of Southern California Los AngelesCA United States

Generalized-linear dynamical models (GLDMs) remain a widely-used framework within neuroscience for modeling time-series data, such as neural spiking activity or categorical decision outcomes. Whereas the standard usage of GLDMs is to model a single data source, certain applications require jointly modeling two generalized-linear time-series sources while also dissociating their shared and private dynamics. Most existing GLDM variants and their associated learning algorithms do not support this capability. Here we address this challenge by developing a multi-step analytical subspace identification algorithm for learning a GLDM that explicitly models shared vs. private dynamics within two generalized-linear time-series. In simulations, we demonstrate our algorithm's ability to dissociate and model the dynamics within two time-series sources while being agnostic to their respective observation distributions. In neural data, we consider two specific applications of our algorithm for modeling discrete population spiking activity with respect to a secondary time-series. In both synthetic and real data, GLDMs learned with our algorithm more accurately decoded one time-series from the other using lower-dimensional latent states, as compared to models identified using existing GLDM learning algorithms. © 2024 Neural information processing systems foundation. All rights reserved.

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Effective short text classification via the fusion of hybrid features for IoT social data

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Digital Communications and Networks 2022年第6期8卷 942-954页

作者： Xiong Luo Zhijian Yu Zhigang Zhao Wenbing Zhao Jenq-Haur Wang School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Shunde Innovation School University of Science and Technology BeijingFoshan528399China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083China Department of Electrical Engineering and Computer Science Cleveland State UniversityClevelandOH44115USA Department of Computer Science and Information Engineering National Taipei University of TechnologyTaipei10608China

Nowadays short texts can be widely found in various social data in relation to the 5G-enabled Internet of Things (IoT). Short text classification is a challenging task due to its sparsity and the lack of context. Previous studies mainly tackle these problems by enhancing the semantic information or the statistical information individually. However, the improvement achieved by a single type of information is limited, while fusing various information may help to improve the classification accuracy more effectively. To fuse various information for short text classification, this article proposes a feature fusion method that integrates the statistical feature and the comprehensive semantic feature together by using the weighting mechanism and deep learning models. In the proposed method, we apply Bidirectional Encoder Representations from Transformers (BERT) to generate word vectors on the sentence level automatically, and then obtain the statistical feature, the local semantic feature and the overall semantic feature using Term Frequency-Inverse Document Frequency (TF-IDF) weighting approach, Convolutional Neural Network (CNN) and Bidirectional Gate Recurrent Unit (BiGRU). Then, the fusion feature is accordingly obtained for classification. Experiments are conducted on five popular short text classification datasets and a 5G-enabled IoT social dataset and the results show that our proposed method effectively improves the classification performance.

关键词： Information fusion Short text classi fication BERT Bidirectional encoder representations fr 0om transformers Deep learning Social data

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Machine learning electron-phonon interactions in two-dimensional semiconducting materials: The case of zero-point renormalization

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Physical Review materials 2024年第10期8卷 L101001页

作者： Anubhab Haldar Quentin Clark Marios Zacharias Feliciano Giustino Sahar Sharifzadeh Department of Electrical and Computer Engineering Department of Philosophy Oden Institute for Computational Engineering and Sciences Department of Physics Division of Materials Science and Engineering

We utilize first-principles theory to investigate the role of electron-phonon interactions within a dataset of monolayer materials. Using density functional theory to describe excited-state transitions and the special displacement method to describe the role of phonons, we analyze the relationship between simple physical observables and electron-phonon coupling strength. For over 100 materials, we compute the band gap renormalization due to zero-point vibrational (ZPR) motion as a measure of electron-phonon interactions and train a machine learning model based on physical parameters. We demonstrate that the strength of electron-phonon interactions is highly dependent on the band gap, dielectric constant, and degree of ionicity, all of which can be physically justified. We then apply this model to 1302 2D materials, predicting the ZPR, which for five randomly selected materials tested agree well with the first-principles predictions. This work provides an approach for quantitatively predicting the ZPR as a measure of electron-phonon interactions in 2D materials.

关键词： Electron-phonon coupling Electronic structure 2-dimensional systems Machine learning

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AlN/GaN/AlN HEMTs on Bulk AlN Substrates with High Drain Current Density > 2.8 A/mm and Average Breakdown Field > 2 MV/cm 82

AlN/GaN/AlN HEMTs on Bulk AlN Substrates with High Drain Cur...

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82nd Device Research Conference, DRC 2024

作者： Kim, Eungkyun Chen, Yu-Hsin Encomendero, Jimy Jena, Debdeep Xing, Huili Grace Cornell University School of Electrical and Computer Engineering IthacaNY14853 United States Cornell University Department of Materials Science and Engineering IthacaNY14853 United States

ISBN: (纸本)9798350373738

Wide energy bandgap and high saturation velocity of GaN have played a pivotal role in increasing the output power density (Pout) of a radio frequency (RF) transistor, allowing for an increased radar and communication system range. A further improvement in output power densities of GaN HEMTs is currently thermally limited due to the waste heat generated during the device operation in the channel layer. Therefore, to realize reliable and sustained operation at high Pout , thermal management in GaN HEMTs must be improved. We present AlN/GaN/AlN HEMTs, where an AlN buffer layer is homoepitaxially grown on bulk AlN substrates. In this heterostructure, AlN simultaneously offers an ultra-wide energy bandgap (~6.1 eV) and high thermal conductivity (340 W/m·K) [1];the thermal advantage of an AlN buffer layer in HEMTs grown on SiC substrates has been recently confirmed by Soctera [2]. AlN homoepitaxy employed in our heterostructure further improves the thermal performance by potentially eliminating the thermal boundary resistance at the growth interface [3]. To this end, we report the on-current and breakdown behavior of HEMTs grown on Al-polar bulk AlN for the first time after the dramatically improved AlN homoepitaxy by MBE [4]. © 2024 IEEE.

关键词： Aluminum nitride

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Correlating the Ambient Conditions and Performance Indicators of the LoRaWAN via Surrogate Gaussian Process-Based Bidirectional LSTM Stacked Autoencoder

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IEEE Transactions on Network and Service Management 2023年第3期20卷 3413-3427页

作者： Bhat, Showkat Ahmad Huang, Nen-Fu Hussain, Imtiyaz Sajjad, Uzair National Tsing Hua University College of Electrical and Computer Science Hsinchu1300044 Taiwan National Tsing Hua University Department of Computer Science Hsinchu1300044 Taiwan National Taipei University of Technology Graduate Program in Energy and Opto-Electronic Materials Taipei10608 Taiwan National Taipei University of Technology Department of Energy and Refrigerating Airconditioning Engineering Taipei10608 Taiwan

LoRa's biggest advantage is its flexibility, which is the ability to increase or decrease data rate and range while decreasing or increasing sensitivity. Whenever propagation conditions change frequently, this function allows the spreading factor to be modified accordingly. Despite their efficiency and scalability, adaptive data rate algorithms ignore and fail to factor in the complex correlation between ambient weather parameters influencing the communication channel design. In this research, a Bayesian surrogate Gaussian process-based bidirectional LSTM stacked autoencoder model (BSGP-BLSTM-SAE) is proposed to estimate the channel performance indicators such as received signal strength indicator (RSSI) and signal-to-noise ratio (SNR) and to determine the correlation between the ambient weather conditions and performance indicators for the LoRaWAN network. Bayesian optimization algorithm has been used to optimize the hyper-parameters of the developed model. A LoRaWAN experimental multivariate time series dataset has been used for the evaluation of the developed model, which upon testing and validation produces high accuracy in predicting the channel performance indicators and ambient conditions of the experimental LoRaWAN network. The mean absolute error of the developed model was around 0.45. Thus, the proposed model can predict the link performance indicators and thereby assist in real-time optimization of the transmission parameters to enhance the network performance in LoRaWAN-based systems at different ambient conditions. © 2004-2012 IEEE.

关键词： Internet of things

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General Optimal Trajectory Planning:Enabling Autonomous Vehicles with the Principle of Least Action

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engineering 2024年第2期33卷 63-76页

作者： Heye Huang Yicong Liu Jinxin Liu Qisong Yang Jianqiang Wang David Abbink Arkady Zgonnikov School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China Department of Cognitive Robotics Faculty of MechanicalMaritime and Materials Engineering(3mE)Delft University of TechnologyDelft 2628Netherlands Department of Software Technology Faculty of Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyDelft 2628Netherlands Xi’an Institute of High-Technology Xi’an 710051China

This study presents a general optimal trajectory planning(GOTP)framework for autonomous vehicles(AVs)that can effectively avoid obstacles and guide AVs to complete driving tasks safely and ***,we employ the fifth-order Bezier curve to generate and smooth the reference path along the road *** coordinates are then transformed to achieve the curvature continuity of the generated *** the road constraints and vehicle dynamics,limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate ***,in selecting the optimal trajectory,we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’behavior and summarizing their manipulation characteristics of“seeking benefits and avoiding losses.”Finally,by integrating the idea of receding-horizon optimization,the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility,optimality,and *** simulations and experiments are performed,and the results demonstrate the framework’s feasibility and effectiveness,which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic ***,we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’manipulation.

关键词： Autonomous vehicle Trajectory planning Multi-performance objectives Principle of least action

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In situ Observation of Lithium Stripping and Plating Process in an Open-cell All-solid-state Lithium Metal Battery

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Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 1287-1288页

作者： Wu, C. Zhao, L. Ai, Q. Risal, S. Lou, J. Yao, Y. Fan, Zheng Materials Science and Engineering Program and Texas Center for Superconductivity at the University of Houston University of Houston Houston TX United States Department of Electrical and Computer Engineering University of Houston Houston TX United States Department of Materials Science and Nano Engineering Rice University Houston TX United States Department of Engineering Technology University of Houston Houston TX United States

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Patterned graphene oxide via one-step thermal annealing for controlling collective cell migration

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Journal of materials Chemistry B 2024年第35期12卷 8733-8745页

作者： Huang, Chien-Yu Chen, Chong-You Wei, Chia-Hung Yang, Jia-Wei Lin, Yu-Chien Kao, Chih-Fei Chung, Johnson H. Y. Chen, Guan-Yu Institute of Biomedical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electrical and Computer Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Biological Science and Technology College of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Intelligent Polymer Research Institute Institute for Innovative Materials University of Wollongong NSW2500 Australia National Yang Ming Chiao Tung University Hsinchu300093 Taiwan

Graphene oxide (GO) is a two-dimensional metastable nanomaterial. Interestingly, GO formed oxygen clusterings in addition to oxidized and graphitic phases during the low-temperature thermal annealing process, which could be further used for biomolecule bonding. By harnessing this property of GO, we created a bio-interface with patterned structures with a common laboratory hot plate that could tune cellular behavior by physical contact. Due to the regional distribution of oxygen clustering at the interface, we refer to it as patterned annealed graphene oxide (paGO). In addition, since the paGO was a heterogeneous interface and bonded biomolecules to varying degrees, arginine-glycine-aspartic acid (RGD) was modified on it and successfully regulated cellular-directed growth and migration. Finally, we investigated the FRET phenomenon of this heterogeneous interface and found that it has potential as a biosensor. The paGO interface has the advantages of easy regulation and fabrication, and the one-step thermal reduction method is suitable for biological applications. We believe that this low-temperature thermal annealing method would make GO interfaces more accessible, especially for the development of nano-interfacial modifications for biological applications, revealing its potential for biomedical applications. © 2024 The Royal Society of Chemistry.

关键词： Biomolecules

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