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Compact Model Build Upon Dynamic Weighting Artificial Neural Network Approach for Complementary Field Effect Transistors

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IEEE Transactions on Electron Devices 2024年第1期71卷 246-253页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation The Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program The Institute of Communications Engineering the Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

In this work, a dynamic weighting-artificial neural network (DW-ANN) methodology is presented for quick and automated compact model (CM) generation. It takes advantage of both TCAD simulations for high accuracy and SPICE simulations for cost-effectiveness. This methodology is developed for gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field effect transistor (CFET), a potential candidate for future CMOS technology due to its innate properties. The critical process variation (PV) sources that severely degrade the CFET performance are estimated using DW-ANN. It reduces the computation cost and predicts the effects of PV sources with less than 1% error. Furthermore, a compact DW-ANN-based Verilog-A model has been developed that captures the dc as well as transient behavior accurately for circuit-level analysis. CFET-based circuits such as inverter, 6T-static random access memory (SRAM), and ring oscillator (RO) are characterized and implemented using DW-ANN-based CM. The overall average error of the model is reported as less than 2%. Therefore, the proposed device and circuit modeling approach provides a feasible solution for the rapid compact modeling of new emerging devices with good convergence and high accuracy. © 1963-2012 IEEE.

关键词： Neural networks

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An Adaptive Metaheuristic Framework for Changing Environments 13

An Adaptive Metaheuristic Framework for Changing Environment...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Ahmed, Bestoun S. Karlstad University Department of Mathematics and Computer Science Karlstad65188 Sweden Czech Technical University in Prague Faculty of Electrical Engineering Department of Computer Science Prague16627 Czech Republic

ISBN: (纸本)9798350308365

The rapidly changing landscapes of modern optimization problems require algorithms that can be adapted in real-time. This paper introduces an Adaptive Metaheuristic Framework (AMF) designed for dynamic environments. It is capable of intelligently adapting to changes in the problem parameters. The AMF combines a dynamic representation of problems, a real-time sensing system, and adaptive techniques to navigate continuously changing optimization environments. Through a simulated dynamic optimization problem, the AMF's capability is demonstrated to detect environmental changes and proactively adjust its search strategy. This framework utilizes a differential evolution algorithm that is improved with an adaptation module that adjusts solutions in response to detected changes. The capability of the AMF to adjust is tested through a series of iterations, demonstrating its resilience and robustness in sustaining solution quality despite the problem's development. The effectiveness of AMF is demonstrated through a series of simulations on a dynamic optimization problem. Robustness and agility characterize the algorithm's performance, as evidenced by the presented fitness evolution and solution path visualizations. The findings show that AMF is a practical solution to dynamic optimization and a major step forward in the creation of algorithms that can handle the unpredictability of real-world problems. © 2024 IEEE.

关键词： Adaptive Metaheuristics Dynamic Optimization Evolutionary Computation Optimization in Changing Environments Real-Time Algorithm Adaptation

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Excitonic Single- and Two-Photon Photoluminescence Excitations in Bilayer GaN/AlN Nanowires

Excitonic Single- and Two-Photon Photoluminescence Excitatio...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Cui, Liangqing Wu, You Wu, Yuanpeng Mi, Zetian Kira, Mackillo Norris, Theodore B. Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States

The energy difference between the 1s and 2p exciton states of bilayer GaN/AlN is measured and computed by comparing excitonic resonances in photoluminescence excitation spectra following single- versus two-photon abso... 详细信息

ISBN: (纸本)9781957171395

关键词： Photoluminescence

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Full-Duplex Millimeter Wave MIMO Channel Estimation: A Neural Network Approach

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 1093-1108页

作者： Sattari, Mehdi Guo, Hao Gündüz, Deniz Panahi, Ashkan Svensson, Tommy Chalmers University of Technology Department of Electrical Engineering Gothenburg412 96 Sweden New York University Tandon School of Engineering Electrical and Computer Engineering Department BrooklynNY11201 United States Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2BX United Kingdom Chalmers University of Technology Department of Computer Science Gothenburg412 96 Sweden

Millimeter wave (mmWave) multiple-input-multi-output (MIMO) is now a reality with great potential for further improvement. We study full-duplex transmissions as an effective way to improve mmWave MIMO systems. Compared to half-duplex systems, full-duplex transmissions may offer higher data rates and lower latency. However, full-duplex transmission is hindered by self-interference (SI) at the receive antennas, and SI channel estimation becomes a crucial step to make the full-duplex systems feasible. In this paper, we address the problem of channel estimation in full-duplex mmWave MIMO systems using neural networks (NNs). Our approach involves sharing pilot resources between user equipments (UEs) and transmit antennas at the base station (BS), aiming to reduce the pilot overhead in full-duplex systems and to achieve a comparable level to that of a half-duplex system. Additionally, in the case of separate antenna configurations in a full-duplex BS, providing channel estimates of transmit antenna (TX) arrays to the downlink UEs poses another challenge, as the TX arrays are not capable of receiving pilot signals. To address this, we employ an NN to map the channel from the downlink UEs to the receive antenna (RX) arrays to the channel from the TX arrays to the downlink UEs. We further elaborate on how NNs perform the estimation with different architectures, (e.g., different numbers of hidden layers), the introduction of non-linear distortion (e.g., with a 1-bit analog-to-digital converter (ADC)), and different channel conditions (e.g., low-correlated and high-correlated channels). Our work provides novel insights into NN-based channel estimators. © 2023 CCBY.

关键词： Millimeter waves

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An All-GaN 800V Three-Level PFC Converter

An All-GaN 800V Three-Level PFC Converter

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Nazerian, Emad Heydari, Mojtaba Yu, Ruiyang Huang, Alex Q. Huang, Qingyun Austin United States University of Missouri Department of Electrical Engineering and Computer Science Columbia United States

ISBN: (纸本)9798350376067

To address the range anxiety issue in battery electric vehicles (BEV), one of the most effective methods is to increase the powertrain voltage. This architecture doubles the charging power at the same current, resulting in faster charging. Today's on-board chargers (OBC) are designed for 400V power train and has lower power level. This paper discusses the design and implementation of a 22kW, 800V bidirectional DC/AC converter for 800V BEVs. Using gallium nitride (GaN) devices significantly improves the efficiency and power density of the converter. However, there's a challenge due to the limited blocking voltage of GaN devices when designing for 800V applications. To tackle this challenge, the paper employed a three-level flying capacitor topology system. This bidirectional PFC system achieves outstanding power density of 203.6W/in3 © 2024 IEEE.

关键词： Gallium nitride

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Federated Multi-Agent Reinforcement Learning for AoI Minimization in UAV-Enabled IoV-MEC Systems 22

Federated Multi-Agent Reinforcement Learning for AoI Minimiz...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Xu, Shoukun Zhang, Yu Wang, Xueyuan Gursoy, M. Cenk Changzhou University Academy of Computer Science and Artificial Intelligence Changzhou China Syracuse University Department of Electrical Engineering and Computer Science SyracuseNY13244 United States

ISBN: (纸本)9798331509712

With the advancement of Mobile Edge Computing (MEC), effective solutions for communication scenarios, including the industrial Internet of Things (IoT) and the Internet of Vehicles (IoV), are becoming increasingly feasible. Unmanned aerial vehicles (UAVs) can further enhance flexibility in delivering computational services within MEC contexts. Addressing the urgent need for information freshness, we propose a three-tier IoV-MEC system, supported by multiple UAVs and a cloud center, aiming to minimize the system's average age of information (AoI). We propose a heterogeneous multi-agent reinforcement learning algorithm based on the actor-critic framework, where vehicles act as data sources, UAVs serve as edge devices, and the cloud acts as a control center. All three tiers learn interaction strategies cooperatively based on the observations. To further enhance system performance, we implement an efficient federated learning method, allowing same-tier agents to share learning parameters, thus improving system performance and convergence speed. Extensive simulation results demonstrate that the proposed algorithm outperforms baseline algorithms in terms of average AoI and convergence speed. © 2024 IEEE.

关键词： Federated learning

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Dependence Induced Representations 60

Dependence Induced Representations

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60th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2024

作者： Xu, Xiangxiang Zheng, Lizhong Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA02139 United States

ISBN: (纸本)9798331541033

We study the problem of learning feature repre-sentations from a pair of random variables, where we focus on the representations that are induced by their dependence. We provide sufficient and necessary conditions for such dependence induced representations, and illustrate their connections to Hirschfeld-Cebelein-Renyi (HGR) maximal correlation functions and minimal sufficient statistics. We characterize a large family of loss functions that can learn dependence induced representations, including cross entropy, hinge loss, and their regularized variants. In particular, we show that the features learned from this family can be expressed as the composition of a loss-dependent function and the maximal correlation function, which reveals a key connection between representations learned from different losses. Our development also gives a statistical interpretation of the neural collapse phenomenon observed in deep classifiers. Finally, we present the learning design based on the feature separation, which allows hyperparameter tuning during inference. © 2024 IEEE.

关键词： Contrastive Learning

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Investigation of XLPE Power Cable Insulation Degradation Under Chemical, electrical, and Mechanical Stress 50

Investigation of XLPE Power Cable Insulation Degradation Und...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Abuelrub, Abdelaziz Refaat, Shady S. Kameli, Sayed Mohammad Department of Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar School of Physics Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom

ISBN: (纸本)9781665464543

High voltage (HV) power cables are exposed to various stresses, such as chemical, electrical, mechanical, and thermal. These stresses contribute to the eventual failure of electrical power cables in an unpredictable manner. Cross-linked polyethylene (XLPE) is an essential component of a power cable’s insulation layers, and unwanted stresses negatively affect the insulation layers’ performance in various ways. In this paper, three 3D Maxwell finite element analysis (FEA) models of a single core, 30 kV DC power cable are built to study the effects of chemical stress, electrical stress, and mechanical stress on the characteristics of polymer insulation material. The main parameter utilized for determining insulation health in this work is the polarization index. Temporal evolution in the polarization index is investigated, as aging-induced stresses degrade the XLPE’s structure. The severity of chemical stress, electrical stress, and mechanical stress is estimated and compared based on the polarization index, with a focus on chemical stress, highlighting the severity of the deformation caused within the inner polymeric structure of XLPE. The severity is obtained for the innermost and outermost insulation layers. It was discovered that the insulation layer’s proximity to the cable’s core is the main factor influencing degradation. Moreover, the obtained results successfully demonstrate the polarization index’s ability to detect degradation after the 95-day mark, indicated by the substantial increase in polarization index values, common between all stress types applied. © 2024 IEEE.

关键词： Crosslinking

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Graph Retrieval-Augmented Generation for Large Language Models: A Survey

Graph Retrieval-Augmented Generation for Large Language Mode...

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2024 IEEE International Conferences of AI, science, engineering, and Technology, AIxSET 2024

作者： Procko, Tyler Thomas Ochoa, Omar Embry-Riddle Aeronautical University Department of Electrical Engineering and Computer Science Daytona Beach United States

ISBN: (纸本)9798350390995

Large Language Models (LLMs) demonstrate general knowledge, but they suffer when specifically needed knowledge is not present in their training set. Two approaches to ameliorating this, without retraining, are 1) prompt engineering and 2) Retrieval-Augmented Generation (RAG). RAG is a form of prompt engineering, insofar as relevant lexical snippets retrieved from RAG corpora are vectorized and aggregated with prompts. However, RAG documents are often noisy, i.e., while relevant to a given prompt, they can contain much other information that obfuscates the desired snippet. If the purpose of pretraining a LLM on massive and general corpora is to engender a generally applicable model, RAG is not: it is a means of LLM optimization, and as such, RAG document selection must be precise, not general. For expert tasks, it is imperative that a RAG corpus be as noise-free as possible, in much the same way a good prompt should be free of irrelevant text. Knowledge Graphs (KGs) provide a concise means of representing domain knowledge free of noisy information. This paper surveys work incorporating KGs with LLM RAG, intending to equip scientists with a better understanding of this novel research area for future work. © 2024 IEEE.

关键词： Knowledge graph

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Quantitative phase imaging endoscopy with a metalens

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Light(science & Applications) 2024年第12期13卷 3196-3209页

作者： Aamod Shanker Johannes E.Fröch Saswata Mukherjee Maksym Zhelyeznyakov Steven L.Brunton Eric J.Seibel Arka Majumdar Department of Electrical and Computer Engineering University of WashingtonSeattleWA 98195USA Center for Vision Science University of RochesterNew YorkNY 14623USA Department of Mechanical Engineering University of WashingtonSeattleWA 98105USA Department of Physics University of WashingtonSeattle 98195USA

Quantitative phase imaging(QPI)recovers the exact wavefront of light from intensity *** and optical density maps of translucent microscopic bodies can be extracted from these quantified phase *** demonstrate quantitative phase imaging at the tip of a coherent fiber bundle using chromatic aberrations inherent in a silicon nitride hyperboloid *** method leverages spectral multiplexing to recover phase from multiple defocus planes in a single capture using a color *** 0.5mm aperture metalens shows robust quantitative phase imaging capability with a 28°field of view and 0.2πphase resolution(~0.1λin air)for experiments with an endoscopic fiber *** the spectral functionality is encoded directly in the imaging lens,the metalens acts both as a focusing element and a spectral *** use of a simple computational backend will enable real-time *** limitations in the adoption of phase imaging methods for endoscopy such as multiple acquisition,interferometric alignment or mechanical scanning are completely mitigated in the reported metalens based QPI.

关键词： phase bundle exact

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