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Recent Progress on Flexible Silicon Nanomembranes for Advanced Electronics and Optoelectronics

ADVANCED FUNCTIONAL MATERIALS 2025年

作者： Zhang, Ziyu Wang, Yang Cai, Tianjun Wu, Binmin Hu, Bofan Li, Xing Song, Enming Huang, Gaoshan Tian, Ziao Di, Zengfeng Mei, Yongfeng Fudan Univ Dept Mat Sci Shanghai 200438 Peoples R China Fudan Univ Shanghai State Key Lab Surface Phys Shanghai 200438 Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol State Key Lab Mat Integrated Circuits Shanghai 200050 Peoples R China Chinese Acad Sci Shanghai Inst Tech Phys State Key Lab Infrared Phys Shanghai 200083 Peoples R China Yiwu Res Inst Fudan Univ Yiwu 322000 Zhejiang Peoples R China Fudan Univ Int Inst Intelligent Nanorobots & Nanosyst Shanghai 200438 Peoples R China Fudan Univ State Key Lab Photovolta Sci & Technol Shanghai 200438 Peoples R China Fudan Univ Inst Optoelect Shanghai Frontiers Sci Res Base Intelligent Optoel Shanghai 200438 Peoples R China

Silicon nanomembranes, an emerging material with ultrathin thickness, combine the electrical properties of semiconductors with the flexibility that bulk materials lack. These nanomembranes can impart enhanced functionality to devices, supporting development needs for next-generation technologies "more-than-Moore" Law. In recent years, as research of fabrication techniques and fundamental principles have advanced, the focus of silicon nanomembrane studies has evolved from material preparation and component processing to functionalization and system-level integration. This review begins with an overview of silicon nanomembrane preparation methods and formation principles. In terms of device advancements and applications, developments in optoelectronic devices, sensors, biomedicine, energy harvesting, and integrated circuits are covered. Finally, the review discusses the current challenges in silicon nanomembrane technology and the potential of silicon nanomembrane devices and systems in future optoelectronics, biomedicine, energy harvesting, and advanced integrated circuit architectures.

关键词： device and system integration energy harvesting photodetectors sensors silicon nanomembrane

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Cardinality-constrained portfolio selection via two-timescale duplex neurodynamic optimization

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NEURAL NETWORKS 2022年第0期153卷 399-410页

作者： Leung, Man-Fai Wang, Jun Che, Hangjun Anglia Ruskin Univ Fac Sci & Engn Sch Comp & Informat Sci Cambridge England City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China City Univ Hong Kong Sch Data Sci Kowloon Hong Kong Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing Peoples R China

This paper addresses portfolio selection based on neurodynamic optimization. The portfolio selection problem is formulated as a biconvex optimization problem with a variable weight in the Markowitz risk-return framework. In addition, the cardinality-constrained portfolio selection problem is formulated as a mixed-integer optimization problem and reformulated as a biconvex optimization problem. A two-timescale duplex neurodynamic approach is customized and applied for solving the reformulated portfolio optimization problem. In the two-timescale duplex neurodynamic approach, two recurrent neural networks operating at two timescales are employed for local searches, and their neuronal states are reinitialized upon local convergence using a particle swarm optimization rule to escape from local optima toward global ones. Experimental results on four datasets of world stock markets are elaborated to demonstrate the superior performance of the neurodynamic optimization approach to three baselines in terms of two major risk-adjusted performance criteria and portfolio returns. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： Neurodynamic optimization Portfolio selection Cardinality constraints

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Smooth Deep Reinforcement Learning for Power Control for Spectrum Sharing in Cognitive Radios

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2022年第12期21卷 10621-10632页

作者： Dang, Lujuan Wang, Wanli Tse, Chi K. K. Lau, Francis C. M. Wang, Shiyuan Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China Hong Kong Polytech Univ Dept Elect & Informat Engn Hong Kong Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China

Spectrum sharing in a cognitive radio system involves a secondary user updating transmit power for sharing spectrum with a primary user. The deep Q-network in the framework of deep reinforcement learning achieves transmit power control by a deep neural network for learning a nonlinear mapping from states to Q-values. Since a deep neural network is confronted with noise susceptibility, the deep Q-network produces deteriorative network parameters and volatile Q-values in the presence of contaminated states. In view of the positive effect of kernel least mean square (KLMS) for signal smoothing, we combine KLMS with the deep Q-network for smoothing network-generated outputs. Since an inappropriate step size of KLMS causes under-smoothing or over-smoothing issues, a weighting procedure using past Q-values is proposed for cooperating with KLMS. We assess the incremental ratio of the success rate of the smooth deep Q-network to that of the deep Q-network (RSR) in cognitive radios. Simulations show that RSR has averaged almost over 30% at the early stage of power control. In particular, the maximum RSR reaches almost over 80% or 180% at different scenarios of power control for the primary user. In addition, the smooth deep Q-network achieves an improved success rate in comparison with other algorithms.

关键词： Spectrum sharing cognitive radio system deep reinforcement learning kernel least mean square weighting procedure

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Latent multi-view subspace clustering based on Laplacian regularized representation 15

Latent multi-view subspace clustering based on Laplacian reg...

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15th International Conference on Advanced Computational Intelligence (ICACI)

作者： Guo, Wei Che, Hangjun Leung, Man-Fai Mu, Nankun Dai, Xiangguang Feng, Yuming Southwest Univ Sch Elect & Informat Engn Chongqing Peoples R China Southwest Univ Sch Elect Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing Peoples R China Anglia Ruskin Univ Fac Sci & Engn Sch Comp & Informat Sci Cambridge England Chongqing Univ Coll Comp Sci Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ Chongqing Peoples R China Chongqing Three Gorges Univ Sch Comp Sci & Engn Chongqing Peoples R China Chongqing Three Gorges Univ Key Lab Intelligent Informat Proc & Control Chongqing Peoples R China

ISBN: (纸本)9798350321456

We propose a latent multi-view subspace clustering model based on Laplacian regularized representation. To emphasize the information of the representation matrix at the local level and reflect the grouping effect of clustering, a Laplacian regularization is imposed on the representation matrix. Additionally, To boost the efficiency of clustering, we apply nonnegative constraints on the representation matrix. A unified framework is designed to solve the proposed model by using ALM and LADMAP methods. The proposed model has excellent performance, as demonstrated by a number of experimental findings.

关键词： subspace clustering nonnegative constraints Laplacian regularization grouping effect

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A distributed accelerated optimization algorithm over time-varying directed graphs with uncoordinated step-sizes

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OPTIMAL CONTROL APPLICATIONS & METHODS 2022年第4期43卷 1182-1200页

作者： Ran, Liang Wang, Chengbo Zheng, Lifeng Li, Huaqing Wang, Zheng Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligen Coll Elect & Informat Engn Chongqing 400715 Peoples R China Southwest Univ Westa Coll Chongqing Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW Australia

This article proposes a distributed accelerated algorithm for solving distributed optimization problems which are defined in a time-varying directed network. Different from the existing algorithms, by implementing row- and column-stochastic matrices, this work eliminates the conservatism in the related work due to doubly-stochastic matrices, and do not required estimate the Perron eigenvector of a stochastic matrix. Assuming that the global objective function is strongly convex and the gradient of each local objective function is Lipschitz-continuous, it is proved that the algorithm converges linearly to the global optimization solution with proper uncoordinated step-sizes and momentum parameters. The numerical simulations are utilized to verify the correctness of the theoretical results and show the practicability of the proposed algorithm.

关键词： distributed optimization heavy-ball method linear convergence multi-agent systems time-varying directed graphs

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A Distributed Nesterov-Like Gradient Tracking Algorithm for Composite Constrained Optimization

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IEEE TRANSACTIONS ON SIGNAL AND informatION PROCESSING OVER NETWORKS 2023年 9卷 60-73页

作者： Zheng, Lifeng Li, Huaqing Li, Jun Wang, Zheng Lu, Qingguo Shi, Yawei Wang, Huiwei Dong, Tao Ji, Lianghao Xia, Dawen Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Minist Educ Key Lab Ind Internet Things & Networked Control Beijing Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing 400000 Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Computat Intelligence Chongqing 400000 Peoples R China Guizhou Minzu Univ Coll Data Sci & Informat Engn Guiyang 550025 Peoples R China

This paper focuses on the constrained optimization problem where the objective function is composed of smooth (possibly nonconvex) and nonsmooth parts. The proposed algorithm integrates the successive convex approximation (SCA) technique with the gradient tracking mechanism that aims at achieving a linear convergence rate and employing the momentum term to regulate update directions in each time instant. It is proved that the proposed algorithm converges provided that the constant step size and momentum parameter are lower than the given upper bounds. When the smooth part is strongly convex, the proposed algorithm linearly converges to the global optimal solution, whereas it converges to a local stationary solution with a sub-linear convergence rate if the smooth part is nonconvex. Numerical simulations are applied to demonstrate the validity of the proposed algorithm and the theoretical analysis.

关键词： Successive convex approximation (SCA) nonconvex optimization Nesterov method gradient tracking distributed optimization

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Comparison of magnetoimpedance behaviors in meander CoFeNiSiB amorphous ribbon for deformation sensing applications

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JOURNAL OF PHYSICS-CONDENSED MATTER 2024年第12期36卷 125802-125802页

作者： Yang, Zhen Liu, Mengyu Luo, Liming Wang, Zhenbao Li, Haihua Sun, Xuecheng Xu, Jun Sun, Xun Lei, Chong Guangxi Normal Univ Sch Elect & Informat Engn Sch Integrated Circuits Gulin 541004 Guangxi Peoples R China Guangxi Normal Univ Educ Dept Guangxi Zhuang Autonomous Reg Key Lab Integrated Circuits & Microsyst Guilin 541004 Guangxi Peoples R China Guangxi Normal Univ Sch Elect & Informat Engn Guangxi Key Lab Brain Inspired Comp & Intelligent Guilin 541004 Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Dept Micro Nanoelect Key Lab Thin Film & Microfabricat Technol Dongchuan Rd 800 Shanghai Peoples R China Shanghai Univ Microelect Res & Dev Ctr Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China Inst Guizhou Aerosp Measuring & Testing Technol Guiyang 550009 Peoples R China

In this work, micro-ribbon strips and meanders based on CoFeNiSiB amorphous ribbons were fabricated by using the lithography technique and chemical etching. Flat and curved holders with different radius of curvature were obtained via 3D printing techniques for GMI testing. Longitudinal and transverse GMI (LGMI and TGMI) behaviors of micro-ribbon sensors in different bending directions and degrees were systematically investigated. The results show the LGMI and TGMI effects of micro-ribbon meanders with one turn is most sensitive to bending. It can be used in the development of deformation sensors. In addition, there is a linear range of field in the LGMI and TGMI curves of micro ribbons under different bending conditions, and the sensitivity of micro-ribbon sensors shows no significant change in the range. In particular, the micro-ribbon meanders with three turns are the least sensitive to bending deformation and can be used to develop stable and flexible GMI sensors for wearable electronics devices.

关键词： Amorphous ribbon LGMI TGMI Meanders Bending

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Primal-Dual Fixed Point Algorithms Based on Adapted Metric for Distributed Optimization

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第6期34卷 2923-2937页

作者： Li, Huaqing Zheng, Zuqing Lu, Qingguo Wang, Zheng Gao, Lan Wu, Guo-Cheng Ji, Lianghao Wang, Huiwei Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Beihang Univ Hangzhou Innovat Inst Hangzhou 310023 Peoples R China Neijiang Normal Univ Coll Math & Informat Sci Data Recovery Key Lab Sichuan Prov Neijiang 641100 Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Computat Intelligence Chongqing 400065 Peoples R China

This article considers distributed optimization by a group of agents over an undirected network. The objective is to minimize the sum of a twice differentiable convex function and two possibly nonsmooth convex functions, one of which is composed of a bounded linear operator. A novel distributed primal-dual fixed point algorithm is proposed based on an adapted metric method, which exploits the second-order information of the differentiable convex function. Furthermore, by incorporating a randomized coordinate activation mechanism, we propose a randomized asynchronous iterative distributed algorithm that allows each agent to randomly and independently decide whether to perform an update or remain unchanged at each iteration, and thus alleviates the communication cost. Moreover, the proposed algorithms adopt nonidentical stepsizes to endow each agent with more independence. Numerical simulation results substantiate the feasibility of the proposed algorithms and the correctness of the theoretical results.

关键词： Optimization Measurement Convex functions Approximation algorithms Signal processing algorithms Convergence Prediction algorithms Adapted metric method completely distributed nonsmooth convex function primal-dual fixed point algorithm randomized asynchronous iterative

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An ultra-violet and infrared dual-band photodetector using a Ga2O3 thin film and HgTe colloidal quantum dots

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NANOSCALE ADVANCES 2025年第8期7卷 2248-2254页

作者： Zheng, Qiqi Yang, Yu Li, Liansheng Xu, Qing-An Zhang, Kenan Xue, Xiaomeng Ma, Lisha Yu, Jianhao Li, Wanjun Chen, Menglu Beijing Inst Technol Sch Opt & Photon Beijing 100081 Peoples R China Optoelect Measurement & Intelligent Percept Zhong Beijing 100190 Peoples R China Westlake Inst Optoelect Zhejiang Key Lab 3D Micro Nano Fabricat & Charact Hangzhou 311421 Zhejiang Peoples R China Chongqing Normal Univ Coll Phys & Elect Engn Chongqing 401331 Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Natl Key Lab Mat Integrated Circuits Shanghai 200050 Peoples R China

Dual-band photodetection of ultraviolet (UV) and infrared (IR) light is an advanced technology aimed at simultaneously or selectively detecting signals from these two distinct wavelength bands. This technique offers broad application prospects, particularly in environments requiring multispectral information. In this work, a solar-blind UV photodetector made from an amorphous Ga2O3 (a-Ga2O3) thin film was combined with a short-wave infrared photodetector made from a HgTe colloidal quantum dot (CQD) film. The photodetector exhibited a high responsivity of up to 1808 A W-1, detectivity of 3.88 x 10(14) Jones, and an external quantum efficiency of 8.8 x 10(5)% at UV wavelength as well as a responsivity of 0.25 A W-1, detectivity of 1.45 x 10(10) Jones, and an external quantum efficiency of 15.5% at short-wave infrared wavelength. Furthermore, corona discharge detection using this photodetector was demonstrated.

关键词： Nanocrystals

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Resource Provision and Allocation Based on Microeconomic Theory in Mobile Edge Computing

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2022年第3期15卷 1512-1525页

作者： Liu, Jiadi Guo, Songtao Liu, Kai Feng, Liang Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Chongqing Univ Coll Comp Sci Minist Educ Key Lab Dependable Serv Comp Cyber Phys Soc Chongqing 400044 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China

Mobile edge computing (MEC) can significantly improve the performance of mobile applications by leveraging nearby servers as the edge cloud to provide task offloading execution service for a smart mobile device (SMD) through wireless access points (APs). However, the edge cloud and AP will not provide free services. Their radio frequency resources and computing resource are limited but the service requests from various mobile devices could be massive. The goal of this article is to provide a pricing mechanism to efficiently allocate limited resources in the MEC system according to the budget of SMDs. To this end, we first present a market model of MEC resources that can give a real insight into the incentives for resource sharing at network edges. In the model, computation, and radio resources can be traded between resource suppliers (AP and edge cloud) and buyers (SMDs). Furthermore, we employ the microeconomic theory to get an optimal budget allocation strategy for the SMD to maximize its utility within a limited budget. Moreover, we propose an Equilibrium Price Finding (EPF) algorithm to find the equilibrium price of the MEC system, maximizing the whole system utility and leading to optimal resource allocation. Finally, simulation results show that, compared with state-of-the-art resource allocation methods, our optimal budget allocation algorithm can find budget allocation strategy more effectively and our equilibrium price finding algorithm can achieve market equilibrium to optimally allocate computation and radio resources in the MEC system.

关键词： Task analysis Resource management Pricing Cloud computing Computational modeling Mobile applications Edge computing Pricing mechanisms resource allocation market equilibrium microeconomic theory mobile edge computing

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