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Thin film ferroelectric photonic-electronic memory

Light(Science & Applications) 2024年第10期13卷 2251-2262页

作者： Gong Zhang Yue Chen Zijie Zheng Rui Shao Jiuren Zhou Zuopu Zhou Leming Jiao Jishen Zhang Haibo Wang Qiwen Kong Chen Sun Kai Ni Jixuan Wu Jiezhi Chen Xiao Gong Department of Electrical and Computer Engineering National University of SingaporeSingapore 119077Singapore Department of Microelectronic Engineering Rochester Institute of TechnologyRochesterNY 14623USA School of Information Science and Engineering Shandong UniversityJinan 250100China

To reduce system complexity and bridge the interface between electronic and photonic circuits,there is a high demand for a non-volatile memory that can be accessed both electrically and ***,practical solutions are still lacking when considering the potential for large-scale complementary metal-oxide semiconductor compatible ***,we present an experimental demonstration of a non-volatile photonic-electronic memory based on a 3-dimensional monolithic integrated ferroelectric-silicon ring *** successfully demonstrate programming and erasing the memory using both electrical and optical methods,assisted by optical-to-electrical-to-optical *** memory cell exhibits a high optical extinction ratio of 6.6 dB at a low working voltage of 5 V and an endurance of 4×10^(4) ***,the multi-level storage capability is analyzed in detail,revealing stable performance with a raw bit-error-rate smaller than 5.9×10^(−2).This ground-breaking work could be a key technology enabler for future hybrid electronic-photonic systems,targeting a wide range of applications such as photonic interconnect,high-speed data communication,and neuromorphic computing.

关键词： resonator electronic ferroelectric

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Transition metal anchored on C_(9)N_(4) as a single-atom catalyst for CO_(2) hydrogenation: A first-principles study

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Chinese Physics B 2022年第10期31卷 520-528页

作者： Jia-Liang Chen Hui-Jia Hu Shi-Hao Wei Department of Microelectronic Science and Engineering School of Physical Science and TechnologyNingbo UniversityNingbo 315211China Department of Electronic and Information Engineering School of Electrical Engineering and Computer ScienceNingbo UniversityNingbo 315211China

To alleviate the greenhouse effect and maintain the sustainable development, it is of great significance to find an efficient and low-cost catalyst to reduce carbon dioxide(CO_(2)) and generate formic acid(FA). In this work, based on the first-principles calculation, the catalytic performance of a single transition metal(TM)(TM = Cr, Mn, Fe, Co, Ni, Cu, Zn,Ru, Rh, Pd, Ag, Cd, Ir, Pt, Au, or Hg) atom anchored on C_(9)N_(4) monolayer(TM@C_(9)N_(4)) for the hydrogenation of CO_(2) to FA is calculated. The results show that single TM atom doping in C_(9)N_(4) can form a stable TM@C_(9)N_(4) structure, and Cu@C_(9)N_(4) and Co@C_(9)N_(4) show better catalytic performance in the process of CO_(2) hydrogenation to FA(the corresponding maximum energy barriers are 0.41 eV and 0.43 e V, respectively). The partial density of states(PDOS), projected crystal orbital Hamilton population(p COHP), difference charge density analysis and Bader charge analysis demonstrate that the TM atom plays an important role in the reaction. The strong interaction between the 3d orbitals of the TM atom and the non-bonding orbitals(1πg) of CO_(2) allows the reaction to proceed under mild conditions. In general, our results show that Cu@C_(9)N_(4) and Co@C_(9)N_(4) are a promising single-atom catalyst and can be used as the non-precious metals electrocatalyst for CO_(2) hydrogenation to formic acid.

关键词： first-principles calculation CO_(2)hydrogenation catalysts electronic structure reaction mechanisms reaction paths

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Synergistic Integration of Machine Learning and Mathematical Optimization for Unit Commitment

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IEEE Transactions on Power Systems 2024年第1期39卷 391-401页

作者： Wu, Jianghua Luh, Peter B. Chen, Yonghong Yan, Bing Bragin, Mikhail A. University of Connecticut Department of Electrical and Computer Engineering StorrsCT06269 United States National Taiwan University Department of Electrical Engineering Taipei10617 Taiwan Miso CarmelIN46032 United States Rochester Institute of Technology Department of Electrical and Microelectronic Engineering RochesterNY14623 United States University of California Department of Electrical and Computer Engineering RiversideCA92507 United States

Unit Commitment (UC) is important for power system operations. With increasing challenges, e.g., growing intermittent renewables and intra-hour net load variability, traditional mathematical optimization could be time-consuming. Machine learning (ML) is a promising alternative. However, directly learning good solutions is difficult in view of the combinatorial nature of UC. This paper synergistically integrates ML within our recent decomposition and coordination method of Surrogate Lagrangian Relaxation to learn 'good enough' subproblem solutions of deterministic UC. Compared to original UC, a subproblem is much easier to learn. Nevertheless, predicting good-enough subproblem solutions is still challenging because of the 'jumps' of binary decisions and many types of constraints. To overcome these issues, subproblem dimensionality is reduced via aggregating multipliers. Multiplier distributions are novelly specified based on 'jumps' for effective learning. Loss functions are innovatively designed to improve prediction qualities. Ordinal Optimization and branch-and-cut are used as backups for unfamiliar cases. Furthermore, online self-learning is seamlessly integrated with offline learning to exploit solutions from daily operations. Results on the IEEE 118-bus system and the Polish 2383-bus system demonstrate that continual learning keeps on improving the subproblem-solving process with near-optimality of the overall solutions maintained. Our method opens a new direction to solve complicated UC. © 1969-2012 IEEE.

关键词： Integer programming

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First Study of the Charge Trapping Aggravation Induced by Anti-Ferroelectric Switching in the MFIS Stack

First Study of the Charge Trapping Aggravation Induced by An...

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2023 IEEE Symposium on VLSI Technology and Circuits, VLSI Technology and Circuits 2023

作者： Zhou, Zuopu Jiao, Leming Zheng, Zijie Wang, Xiaolin Zhang, Dong Ni, Kai Gong, Xiao Department of Electrical and Computer Engineering 117576 Singapore Rochester Institute of Technology Department of Electrical and Microelectronic Engineering RochesterNY14623 United States

ISBN: (纸本)9784863488069

In this work, we perform systematic and deep understanding of the interplay between polarization switching and charge trapping (CT) for both ferroelectric (FE) metal-ferroelectric-insulator-semiconductor (MFIS) stack and anti-ferroelectric (AFE) MFIS stack. Assisted by the simulation with a unified FE/AFE model and CT model, the different roles of FE and AFE switching in the CT process are unraveled, high-lighting the aggravated CT effect induced by the AFE. We further build a comprehensive FeFET model considering (a) phase fluctuation among FE/AFE/dielectric (DE) phases, (b) EC variation, (c) un-uniform CT process, and (d) channel percolation, which predicts a more severe memory window (MW) degradation caused by the AFE phase compared with DE phase. The results emphasize the necessity of suppressing the AFE phase for the optimization of FeFETs. © 2023 JSAP.

关键词： Charge trapping

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Exploration of Thermoelectric Energy Harvesting for Secure, TLS-Based Industrial IoT Nodes 1

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7th International Conference on Internet of Things, ICIOT 2022 held as Part of the Services Conference Federation, SCF 2022

作者： Lauer, Frederik Schöffel, Maximilian Rheinländer, Carl C. Wehn, Norbert Department of Electrical and Computer Engineering Microelectronic Systems Design Research Group Technische Universität Kaiserslautern Kaiserslautern67663 Germany

ISBN: (数字)9783031235825

ISBN: (纸本)9783031235818

Security is one of the biggest challenges, particularly in the Industrial IoT and in critical infrastructures. Complex cryptographic computations are in contrast to the low energy budget of the devices, especially when independence from the power grid is required, as it is the case with retrofitted sensor nodes. Energy harvesting offers a promising alternative but tightens the energy constraints of the application further. In this work, we investigate how IoT edge devices can be powered by thermal energy harvesting and concurrently meet the stringent TLS-based security requirements. We analyze a thermoelectric generator system at its lowest power output region and evaluate different energy storage technologies in a representative IoT architecture. Our results show that temperature gradients as low as 1 K are sufficient to enable secure connections every 20 min in a representative IIoT application. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Energy harvesting

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Integrating Machine Learning and Mathematical Optimization for Job Shop Scheduling

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IEEE Transactions on Automation Science and engineering 2024年第3期21卷 4829-4850页

作者： Liu, Anbang Luh, Peter B. Sun, Kailai Bragin, Mikhail A. Yan, Bing Department of Automation and BNRist Beijing100084 China University of Connecticut Department of Electrical and Computer Engineering StorrsCT06269 United States National Taiwan University Department of Electrical Engineering Taipei10617 Taiwan Tsinghua University Chaired Professors Group Center for Intelligent and Networked Systems Department of Automation Beijing100084 China Rochester Institute of Technology Department of Electrical and Microelectronic Engineering RochesterNY14623 United States

Job-shop scheduling is an important but difficult combinatorial optimization problem for low-volume and high-variety manufacturing, with solutions required to be obtained quickly at the beginning of each shift. In view of the increasing demand for customized products, problem sizes are growing. A promising direction is to take advantage of Machine Learning (ML). Direct learning to predict solutions for job-shop scheduling, however, suffers from major difficulties when problem scales are large. In this paper, a Deep Neural Network (DNN) is synergistically integrated within the decomposition and coordination framework of Surrogate Lagrangian Relaxation (SLR) to predict good-enough solutions for subproblems. Since a subproblem is associated with a single part, learning difficulties caused by large scales are overcome. Nevertheless, the learning still presents challenges. Because of the high-variety nature of parts, the DNN is desired to be able to generalize to solve all possible parts. To this end, our idea is to establish 'surrogate' part subproblems that are easier to learn, develop a DNN based on Pointer Network to learn to predict their solutions, and calculate the solutions of the original part subproblems based on the predictions. Moreover, a masking mechanism is developed such that all the predictions are feasible. Numerical results demonstrate that good-enough subproblem solutions are predicted in many iterations, and high-quality solutions of the overall problem are obtained in a computationally efficient manner. The performance of the method is further improved through continuous learning. Note to Practitioners - Scheduling is important for the planning and operation of job shops, and high-quality schedules need to be obtained quickly at the beginning of each shift. To take advantage of ML, in this paper, a DNN is integrated within our recent decomposition and coordination approach to learn to predict 'good-enough' solutions to part subproblems. To be able t

关键词： Deep neural networks

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Near-Optimal Scheduling for IC Packaging Operations Considering Processing-Time Variations and Factory Practices

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IEEE Robotics and Automation Letters 2024年第4期9卷 3878-3885页

作者： Tsai, Tsung-Han Yan, Bing Luh, Peter B. Yang, Haw-Ching Bragin, Mikhail A. Cheng, Fan-Tien National Cheng Kung University Institute of Manufacturing Information and Systems Tainan701401 Taiwan Rochester Institute of Technology Department of Electrical and Microelectronic Engineering RochesterNY14623 United States University of Connecticut Department of Electrical and Computer Engineering StorrsCT06269 United States National Taiwan University Department of Electrical Engineering Taipei106329 Taiwan National Kaohsiung University of Science and Technology Department of Electrical Engineering Kaohsiung824005 Taiwan University of California Department of Electrical and Computer Engineering RiversideCA92507 United States

Due to the short life cycles of electronic products, trial run lots of new products are crucial in IC packaging for production verification and engineering adjustments. The processing time of trial run lots may differ significantly from production lots due to engineering adjustments and is difficult to predict without sufficient historical data. The purpose of this letter is to develop a framework for near-optimal scheduling of IC packaging considering processing time variations and factory practices. Using an orthogonal greedy algorithm and recurrent neural network, this framework extracts key features and predicts the processing times of trial run lots and production lots for each operation using these algorithms. After formulating the scheduling problem in an integer linear programming form, an ordinal optimization method is embedded within the decomposition and coordination framework to obtain dynamic and near-optimal schedules in a computationally efficient manner. This approach can improve the efficiency of IC packaging and potentially other operations by optimizing lot scheduling, reducing production tardiness, and increasing productivity in the factory, paving the way for self-optimizing factories in the future. © 2016 IEEE.

关键词： Integer programming

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Temperature dependent implantation of As-Isoelectronic Impurity in GaN Micro and Nanostructures

Temperature dependent implantation of As-Isoelectronic Impur...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Waseem, Aadil Chan, Clarence Wu, Xihang Liu, Yujie Shen, Yifan Kapritsos, Manos Norris, Theodore B. Li, Xiuling Microelectronic Research Center Chandara Family Department of Electrical and Computer Engineering The University of Texas at Austin AustinTX78758 United States Department of Electrical and Computer Engineering Holonyak Micro and Nanotechnology Laboratory University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States

To enhance solar photo absorption of III-N semiconductors, we demonstrate Arsenic implantation at elevated temperatures, improving doping activation and reducing crystal damage. CLEO 2024 © Optica Publishing Grou... 详细信息

ISBN: (纸本)9781957171395

关键词： Gallium nitride

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Temperature dependent implantation of As-Isoelectronic Impurity in GaN Micro and Nanostructures

Temperature dependent implantation of As-Isoelectronic Impur...

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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： Gallium nitride

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Cross-Layer Optimizations for Ferroelectric Neuromorphic Computing 66

Cross-Layer Optimizations for Ferroelectric Neuromorphic Com...

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2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023

作者： M Nafiul Islam, A.N. Ni, Kai Sengupta, Abhronil School of Electrical Engineering and Computer Science The Pennsylvania State University University ParkPA16802 United States Rochester Institute of Technology Electrical & Microelectronic Engineering Department RochesterNY14623 United States

ISBN: (纸本)9798350302103

Hardware paradigms for neuromorphic computing mimic the functionalities of brain primitives in order to replicate similar area and energy advantages of biological nervous systems. Discovery of ferroelectricity in doped Hafnia has thrust Ferroelectronics, specifically Ferroelectric Field-effect transistors (FeFETs), at the forefront of realizing such device platforms for future data-centric applications. Despite the utility afforded by its intrinsic properties, e.g. CMOS-compatibility and scalability, harnessing their full potential requires a cross-layer design approach combining devices, circuits, and algorithms. In this paper, we review the recent developments looking at FeFETs and their application to enable low-power on-chip learning. We outline the unique opportunities emerging from device characterization and modelling results, that ultimately translate in novel algorithms and system-level benefits. © 2023 IEEE.

关键词： cross-layer optimization Ferroelectric field-effect transistor (FeFET) neuromorphic computing on-chip learning

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