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IEEE Open Journal of Nanotechnology 2024年 5卷 9-16页

作者： Aslam, Muhammad Chang, Shu-Wei Chuang, Min-Hui Chen, Yi-Ho Lee, Yao-Jen Li, Yiming Parallel and Scientific Computing Laboratory National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electrical Engineering National Cheng Kung University Tainan701401 Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Graduate Degree Program of College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Biomedical Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan

Recently, a-IGZO has advanced toward the next-generation electronics system because of its compatibility with complementary metal oxide semiconductor (CMOS) and back-end-of-line (BOEL) based systems. A systematic electrical characterization of a-IGZO TFT related to reliability issues, such as positive bias temperature stress (PBTS) and negative bias temperature stress (NBTS), would entitle its integration into novel electronics systems. Unexpectedly, PBTS is characterized by the transition of positive Vth shift to negative Vth shift (ΔVth, the positive shift followed by the stress and temperature activated negative shift). This transition is attributed to charge trapping/trap-site generations and hydrogen migration to the active layer. The ΔVth shift mechanism depends on the temperature and voltage stress. On the other hand, a negative ΔVth shift has been observed during the NBTS operation and could be attributed to the hole trapping at the interface of GI/IGZO. An effective suppression of the gate leakage current has also been observed during reliability tests. Simulation results reveal a pronounced potential at the edges of source and drain regions, and considered the origin of hydrogen migration into the IGZO layer. Thermal image results also reveal the strong temperature/potential distribution at the edges of the source/drain regions, indorsing the simulation results. © 2020 IEEE.

关键词： Oxide semiconductors

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Graph Coordinates and Conventional Neural Networks - An Alternative for Graph Neural Networks

Graph Coordinates and Conventional Neural Networks - An Alte...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Qin, Zheyi Paffenroth, Randy Jayasumana, Anura P. Colorado State University Electrical and Computer Engineering Fort Collins United States Worcester Polytechnic Institute Department of Data Science Program WorcesterMA United States

ISBN: (纸本)9798350324457

Graph-based data present unique challenges and opportunities for machine learning. Graph Neural Networks (GNNs), and especially those algorithms that capture graph topology through message passing for neighborhood aggregation, have been a leading solution. However, these networks often require substantial computational resources and may not optimally leverage the information contained in the graph's topology, particularly for large-scale or complex *** propose Topology Coordinate Neural Network (TCNN) and Directional Virtual Coordinate Neural Network (DVCNN) as novel and efficient alternatives to message passing GNNs, that directly leverage the graph's topology, sidestepping the computational challenges presented by competing algorithms. Our proposed methods can be viewed as a reprise of classic techniques for graph embedding for neural network feature engineering, but they are novel in that our embedding techniques leverage ideas in Graph Coordinates (GC) that are lacking in current *** results, benchmarked against the Open Graph Benchmark Leaderboard, demonstrate that TCNN and DVCNN achieve competitive or superior performance to message passing GNNs. For similar levels of accuracy and ROC-AUC, TCNN and DVCNN need far fewer trainable parameters than contenders of the OGBN Leaderboard. The proposed TCNN architecture requires fewer parameters than any neural network method currently listed in the OGBN Leaderboard for both OGBN-Proteins and OGBN-Products datasets. Conversely, our methods achieve higher performance for a similar number of trainable parameters. These results hold across diverse datasets and edge features, underscoring the robustness and generalizability of our methods. By providing an efficient and effective alternative to message passing GNNs, our work expands the toolbox of techniques for graph-based machine learning. A significantly lower number of tunable parameters for a given evaluation metric makes TCNN and DVCNN especiall

关键词： graph analytics graph coordinate graph embedding green AI network embedding

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Filament-free Bulk RRAM with High Endurance and Long Retention for Neuromorphic Few-Shot Learning On-Chip

Filament-free Bulk RRAM with High Endurance and Long Retenti...

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2024 IEEE International Electron Devices Meeting, IEDM 2024

作者： Kumar, Ashwani Zhou, Yucheng Potladurthy, Sai Praneeth Kim, Jeong-Hoon Xu, Weihong Ponzina, Flavio Kim, Seonghyun Cubukcu, Ertugrul Rosing, Tajana Cauwenberghs, Gert Kuzum, Duygu Department of Electrical and Computer Engineering University of California San DiegoCA United States Department of Bioengineering University of California San DiegoCA United States Department of Computer Science University of California San DiegoCA United States Department of NanoEngineering University of California San DiegoCA United States SK Hynix Korea Republic of

ISBN: (纸本)9798350365429

Bulk switching RRAM devices have emerged to address nonidealities of filamentary RRAM for AI at the edge. However, long retention and high endurance needed for continual on-chip learning have yet to be demonstrated. Here, we report a filament-free and multi-level bulk RRAM (b-RRAM) technology with long retention and high endurance. The oxygen vacancy gradient across the TiOx switching layer and Al2O3 tunnel barrier are designed to suppress filament formation and achieve multi-level switching through modulation of oxygen vacancy distribution. Al-Ti-O oxygen barrier enables reliable switching with high uniformity, retention and endurance. We develop a compact model that captures both DC and pulse switching. MΩ-level resistance and switching current nonlinearity allow highly accurate read/write operations and matrix vector multiplications (MVM) in selectorless b-RRAM crossbars. A neuro-inspired few-shot learning (FSL) algorithm based on dendritic computation and behavioral time plasticity (BTSP) is mapped onto b-RRAM crossbar arrays. Weights are continuously updated on-chip to learn shortest navigation to reward within a few trials in a 2D maze. We also present FSL of large-size images with hyperdimensional (HD) computing using our b-RRAM model. System-level simulations with 64-level b-RRAM arrays show an increase in accuracy by 5.9 % and 5.4 % for a 5-way 10-shot learning of CIFAR-100 and CUB-200 datasets as compared to the state-of-the-art FSL-HD. © 2024 IEEE.

关键词： RRAM

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High-quality Mid-infrared Chalcogenide Ring Resonator

High-quality Mid-infrared Chalcogenide Ring Resonator

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Lu, Bright Xu, Bo Krueper, Greg Zohrabi, Mo Gopinath, Juliet T. Park, Wounjhang Department of Electrical Computer and Energy Engineering University of Colorado BoulderCO80309 United States Department of Physics University of Colorado BoulderCO80309 United States Materials Science and Engineering Program University of Colorado BoulderCO80309 United States

ISBN: (纸本)9781957171395

We report Ge23Sb7S70 chalcogenide ring resonators with up to 8 × 104 quality factors operating around 3.6 µm wavelength fabricated through e-beam lithography. Their rib waveguide geometry can be engineered to support close-to-zero dispersion modes needed for mid-infrared microcomb generation. CLEO 2024 © Optica Publishing Group 2024 © 2024 The Author(s)

关键词： Ring lasers

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High-quality Mid-infrared Chalcogenide Ring Resonator

High-quality Mid-infrared Chalcogenide Ring Resonator

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CLEO: science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

关键词： Infrared devices

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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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Short Wave Infrared Upconversion Imager with Electron Blocking Layer to Screen Electron Injection into Photosensitive Layer 5

Short Wave Infrared Upconversion Imager with Electron Blocki...

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5th IEEE International Conference on Flexible and Printable Sensors and Systems, FLEPS 2023

作者： Shin, Chanho Li, Ning Ng, Tse Nga University of California San Diego Material Science and Engineering Program La Jolla United States University of California San Diego Department of Electrical and Computer Engineering La Jolla United States

ISBN: (纸本)9781665457330

In this work, we demonstrated upconversion imagers integrated with shortwave infrared photodetectors paired with an electron blocking layer. The use of electron blocking layer screened charge injection to prevent recombination in photosensitive layer. The characteristics of each electron blocking layer were analyzed in aspects of noise and detectivity. For the optimized device, the parasitic luminance in the dark was efficiently suppressed, and the photon-to-photon efficiency was increased. The electron blocking layer used in this work is generally applicable for upconversion imagers using different absorption and emitting materials. © 2023 IEEE.

关键词： Photodetectors

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Single-photon quantum effects in biomolecules: Quantum biological spectroscopy

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Nature Photonics 2025年第2期19卷 129-130页

作者： Lee, Luke P. Harvard Medical School Harvard University Brigham and Women’s Hospital BostonMA United States Department of Bioengineering University of California Berkeley BerkeleyCA United States Department of Electrical Engineering and Computer Science University of California Berkeley BerkeleyCA United States Department of Biophysics Institute of Quantum Biophysics Sungkyunkwan University Suwon Korea Republic of Department of Chemistry and Nanoscience Ewha Womans University Seoul Korea Republic of

The effects of magnetic fields on quantum entangled biomolecules are detected using a highly sensitive magneto-fluorescence fluctuation microspectroscopy technique. This marks an important advance in experimental quan... 详细信息

关键词： Quantum entanglement

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Multivariate Time-Series Deep Learning for Joint Prediction of Temperature and Relative Humidity in a Closed Space 8

Multivariate Time-Series Deep Learning for Joint Prediction ...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Gunawan, Fergianto E. Budiman, Arief S. Pardamean, Bens Juana, Endang Romeli, Sugiarto Cenggoro, Tjeng W. Purwandari, Kartika Hidayat, Alam A. Redi, Anak A.N.P. Asrol, Muhammad Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia PT Impack Pratama Industri Jakarta14350 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of deep learning on time-series data, developing a predictive temperature and humidity model with deep learning is propitious. In this study, we demonstrated that deep learning models with multivariate time-series data produce remarkable performance for temperature and relative humidity prediction in a closed space. In detail, all deep learning models that we developed in this study achieve almost perfect performance with an R value over 0.99. © 2023 The Authors. Published by Elsevier B.V.

关键词： closed space deep learning humity prediction indoor temperature prediction

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Multi-gate FeFET Discriminates Spatiotemporal Pulse Sequences for Dendrocentric Learning

Multi-gate FeFET Discriminates Spatiotemporal Pulse Sequence...

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2023 International Electron Devices Meeting, IEDM 2023

作者： Chen, Hugo J.-Y. Beauchamp, Matthew Toprasertpong, Kasidit Huang, Fei Le Coeur, Louis Nemec, Thorgund Wong, H.-S. Philip Boahen, Kwabena GlobalFoundries Dresden Germany Stanford University Department of Electrical Engineering StanfordCA United States Stanford University Department of Bioengineering StanfordCA United States Stanford University Department of Computer Science StanfordCA United States

ISBN: (纸本)9798350327670

This paper presents a dendrite-like device that discriminates spatiotemporal patterns of pulses for parallel processing in 3D neuromorphic architectures. The device utilizes the ferroelectric layer in a segmented multi-gate FeFET design to detect a consecutive sequence of input pulses. Experimental results demonstrate successful emulation of highly selective sequence discrimination in dendrites of neurons in the cortex and highlight up to 100× signal-margin (output current differences). This nanodendrite design offers a neuromorphic solution to thermally scalable parallel processing in 3D systems. © 2023 IEEE.

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