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21st International Conference on Miniaturized systems for Chemistry and Life Sciences, MicroTAS 2017

作者： Hun, Tingting Liu, Yaoping Guo, Yechang Liu, Xiaoyi Wang, Wei Sun, Yan Fan, Yubo School of Biological Science and Medical Engineering Beihang University Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education Beijing100191 China Institute of Microelectronics Peking University Beijing100871 China National Key Laboratory of Science and Technology on Micro/Nano Fabrication Beijing100871 China Innovation Center for Micro-Nano-electronics and Integrated System Beijing100871 China State Key Laboratory of Transducer Technology Chinese Academy of Sciences Shanghai200050 China

ISBN: (纸本)9780692941836

Investigations on single-cell level are significant in cellular researches, and the captured of single cell have been reported by many researchers. However, there is still a lack of effective methods to realize high-throughput cell capture and arrangement simultaneously. In this paper, we compared three micropattern methods for high-throughput cell capture and arrangement, and we found a micropore-arrayed membrane based micropattern method could reach efficiency higher than 90%. © 17CBMS-0001.

关键词： Cytology

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Mechanical Characterization of Cells and Microspheres Sorted by Acoustophoresis with In-Line Resistive Pulse Sensing

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Physical Review Applied 2020年第3期13卷 034058-034058页

作者： Antoine Riaud Wei Wang Anh L.P. Thai Valerie Taly Centre de Recherche des Cordeliers INSERM CNRS Sorbonne Université USPC Université Paris Descartes Université Paris Diderot F-75006-Equipe labellisée Ligue Nationale contre le cancer Paris France ASIC and System State Key Laboratory School of Microelectronics Fudan University Shanghai 200433 China

Resistive pulse sensing (RPS) is a key label-free technology to measure particles and single-cell size distribution. As a growing corpus of evidence supports that cancer cells exhibit distinct mechanical phenotypes from healthy cells, expanding the method from size to mechanical sensing could represent a pertinent and innovative tool for cancer research. In this paper, we infer the cells’ compressibility by using acoustic radiation pressure to deflect flowing cells in a microchannel, and use RPS to sense the subpopulations of cells and particles at each acoustic power level. Compared to current acoustic cell phenotyping apparatus based on video cameras, the proposed approach is not limited by optical diffraction, frame rate, data storage, or processing speed, and may ultimately constitute a step forward towards point-of-care acousto-electrical phenotyping and acoustic phenotyping of liquid biosamples.

关键词： Acoustic interactions Cell mechanics Metrology Microfluidics Nonlinear acoustics Cells Acoustic techniques Resistivity measurements

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Learning Low-Rank Structured Sparsity in Recurrent Neural Networks

Learning Low-Rank Structured Sparsity in Recurrent Neural Ne...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Weijing Wen Fan Yang Yangfeng Su Dian Zhou Xuan Zeng State Key Lab of ASIC & System School of Microelectronics Fudan University China School of Mathematical Sciences Fudan University China Department of Electrical Engineering University of Texas at Dallas Richardson TX U.S.A

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

Acceleration and wide deployability in deeper recurrent neural network is hindered by high demand for computation and memory storage on devices with memory and latency constraints. In this work, we propose a novel regularization method to learn hardware-friendly sparse structures for deep recurrent neural networks. Considering the consistency of dimension in continuous time units in recurrent neural networks, low-rank structured sparse approximations of the weight matrices are learned through the regularization without dimension distortion. Our method is architecture agnostic and can learn compact models with higher degree of sparsity than the state-of-the-art structured sparsity learning method. The structured sparsity rather than random sparsity also facilitates the hardware implementation. Experiments on language modeling of Penn TreeBank dataset show that our approach can reduce the parameters of stacked recurrent neural network model by over 90% with less than 1% perplexity loss. It is also successfully evaluated on larger highway neural network model with word2vec dataset like enwik8 and text8 using only 20M weights.

关键词： Recurrent neural networks Computer architecture Sparse representation Road transportation Hardware Logic gates

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Energy Management and Trajectory Optimization for UAV-Enabled Legitimate Monitoring systems

arXiv

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arXiv 2020年

作者： Hu, Shuyan Wu, Qingqing Wang, Xin State Key Laboratory of ASIC and System School of Information Science and Technology Fudan University Shanghai200433 China Department of Electrical and Computer Engineering National University of Singapore Singapore119077 Singapore State Key Laboratory of ASIC and System Shanghai Institute for Advanced Communication and Data Science Department of Communication Science and Engineering Fudan University Shanghai200433 China

Thanks to their quick placement and high flexibility, unmanned aerial vehicles (UAVs) can be very useful in the current and future wireless communication systems. With a growing number of smart devices and infrastructure-free communication networks, it is necessary to legitimately monitor these networks to prevent crimes. In this paper, a novel framework is proposed to exploit the flexibility of the UAV for legitimate monitoring via joint trajectory design and energy management. The system includes a suspicious transmission link with a terrestrial transmitter and a terrestrial receiver, and a UAV to monitor the suspicious link. The UAV can adjust its positions and send jamming signal to the suspicious receiver to ensure successful eavesdropping. Based on this model, we first develop an approach to minimize the overall jamming energy consumption of the UAV. Building on a judicious (re)formulation, an alternating optimization approach is developed to compute a locally optimal solution in polynomial time. Furthermore, we model and include the propulsion power to minimize the overall energy consumption of the UAV. Leveraging the successive convex approximation method, an effective iterative approach is developed to find a feasible solution fulfilling the Karush-Kuhn-Tucker (KKT) conditions. Extensive numerical results are provided to verify the merits of the proposed schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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A physically unclonable function with BER < 0.35% for secure chip authentication using write speed variation of RRAM 48

A physically unclonable function with BER < 0.35% for secure...

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48th European Solid-state Device Research Conference, ESSDERC 2018

作者： Yang, Jianguo Li, Xing Wang, Tao Xue, Xiaoyong Hong, Zhiliang Wang, Yuanyuan Zhang, David Wei Lu, Hongliang ASIC and System State Key Laboratory School of Microelectronics Fudan University Shanghai200433 China

ISBN: (纸本)9781538654019

We propose a reliable design of physically unclonable function (PUF) utilizing the write speed variation of RRAM for the first time. The demonstration is carried out on a 256Kb test macro of HfO2/WOx bi-layer RRAM which was fabricated based on 0.13 μm logic process. The proposed RRAM PUF is capable of regenerating more than 1015 times after 10 years at 125°C. The bit error rate (BER) remains © 2018 IEEE.

关键词： RRAM

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Acoustic radiation force on small spheres due to transient acoustic fields

arXiv

引用

arXiv 2020年

作者： Riaud, Antoine Wang, Qing Gong, Zhixiong Baudoin, Michael Zhou, Jia State Key Laboratory of ASIC and System School of Microelectronics Fudan University Shanghai200433 China Univ. Lille CNRS Centrale Lille Univ. Polytechnique Hauts-de-France UMR 8520 IEMN LilleF-59000 France

Acoustic radiation force is a net force experienced by an object under the action of an acoustic wave. Most theoretical models require the acoustic wave to be periodic, if not purely monofrequency, and are therefore irrelevant for the study of acoustic radiation force due to acoustic pulses. Here, we introduce the concept of finite-duration pulses, which is the most general condition to derive the acoustic radiation force. In the case of small spheres, we extend the Gor’kov to formula to unsteady acoustic fields such as traveling pulses and interfering wave packets. In the latter case, our study suggests that the concept of acoustic contrast is also relevant to express the acoustic radiation force. For negative acoustic contrast particles, the acoustic trapping region narrows with shorter pulses, whereas positive contrast particles (such as biological cells) can fall in secondary traps when the pulse width deviates from an optimal value. This theoretical insight may help to improve the selectivity of pulsed acoustic tweezers. Copyright © 2020, The Authors. All rights reserved.

关键词： Acoustic fields

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An Efficient Bayesian Optimization Approach for Analog Circuit Synthesis via Sparse Gaussian Process Modeling

An Efficient Bayesian Optimization Approach for Analog Circu...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Biao He Shuhan Zhang Fan Yang Changhao Yan Dian Zhou Xuan Zeng State Key Lab of ASIC & System School of Microelectronics Fudan University Shanghai P. R. China Department of Electrical Engineering University of Texas at Dallas Richardson TX U.S.A.

ISBN: (数字)9783981926347

ISBN: (纸本)9781728144689

Bayesian optimization with Gaussian Process (GP) models has been proposed for analog synthesis since it is efficient for the optimizations of expensive black-box functions. However, the computational cost for training and prediction of Gaussian process models are O(N 3 ) and O(N 2 ), respectively, where N is the number of data points. The overhead of the Gaussian process modeling would not be negligible as N is relatively large. Recently, a Bayesian optimization approach using neural network has been proposed to address this problem. It reduces the computational cost of training and prediction of Gaussian process models to O(N) and O(1), respectively. However, reducing the infinite-dimensional kernel to finite-dimensional kernel using neural network mapping would weaken the characterization ability of Gaussian process. In this paper, we propose a novel Bayesian optimization approach using Sparse Pseudo-input Gaussian Process (SPGP). The idea is to use M

关键词： Optimization Gaussian processes Computational modeling Bayes methods Integrated circuit modeling Training Predictive models

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Binary Convolutional Neural Network Based on 3d Field-Programmable Ferroelectric Diode Array

SSRN

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SSRN 2020年

作者： Luo, Qing Xue, Xiaoyong Chen, Bing Yu, Haoran Fang, Jinbei Cheng, Yan Zheng, Yunzhe Gong, Tiancheng Yu, Jie Xu, Xiaoxin Yang, Jianguo Yuan, Peng Tai, Lu Chen, Jiezhi Lv, Hangbing Liu, Ming Key Laboratory of Microelectronics Devices and Integrated Technology Institute of Microelectronics of the Chinese Academy of Sciences China State Key Laboratory of ASIC and System Fudan University China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China East China Normal University Shanghai China University of Chinese Academy of Sciences School of Information Science and Engineering Shandong University

Ubiquitous artificial intelligence (AI) applications call for increasing computing power and large communication bandwidth, which are challenging to meet given the diminishing benefits from traditional planar CMOS scaling. Three-dimension (3D) integration of integrated circuits (IC) promises to achieve further performance improvements at reduced power and smaller footprint. Here, we report a 3D array of 8 layers of field-programmable ferroelectric diode (FPD) for binary convolutional neural network (BCNN) in energy-efficient AI applications. The convolutional kernels are duplicated and stored in the pillars in 3D FPD array to providing high computation parallelism. An AND-based logic using FPD is proposed to realize the XNOR function in BCNN. As a proof-of-concept demonstration, a BCNN is constructed based on the 3D FPD array for MNIST test and qualified to present the advantages of our proposal in 28nm logic process design kit. © 2020, The Authors. All rights reserved.

关键词： Ferroelectricity

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An Efficient Asynchronous Batch Bayesian Optimization Approach for Analog Circuit Synthesis

An Efficient Asynchronous Batch Bayesian Optimization Approa...

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Design Automation Conference

作者： Shuhan Zhang Fan Yang Dian Zhou Xuan Zeng State Key Lab of ASIC & System School of Microelectronics Fudan University Shanghai P. R. China Department of Electrical Engineering University of Texas at Dallas Richardson TX U.S.A.

ISBN: (数字)9781728110851

ISBN: (纸本)9781728158020

In this paper, we propose EasyBO, an Efficient ASYn-chronous Batch Bayesian Optimization approach for analog circuit synthesis. In this proposed approach, instead of waiting for the slowest simulations in the batch to finish, we accelerate the optimization procedure by asynchronously issuing the next query points whenever there is an idle worker. We introduce a new acquisition function which can better explore the design space for asynchronous batch Bayesian optimization. A new strategy is proposed to better balance the exploration and exploitation and guarantee the diversity of the query points. And a penalization scheme is proposed to further avoid redundant queries during the asynchronous batch optimization. The efficiency of optimization can thus be further improved. Compared with the state-of-the-art batch Bayesian optimization algorithm, EasyBO achieves up to 7.35× speed-up without sacrificing the optimization results.

关键词： Optimization Bayes methods Analog circuits Mathematical model Computational modeling Space exploration Uncertainty

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Anti-lotus leaf effect: smearing millions of picoliter droplets on bio-inspired artificial lotus leaf

Anti-lotus leaf effect: smearing millions of picoliter dropl...

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IEEE Conference on Nanotechnology

作者： Lin Du Yongheng Wei Antoine Riaud Jia Zhou ASIC and System State Key Lab School of Microelectronics Fudan University China State Key Laboratory of Genetic Engineering Fudan University China

Compartmentalization of aqueous samples into micro-droplets enables to study single cells or detect single molecules by performing large number of independent experiments. However, as the droplet size becomes smaller, the surface energy becomes larger so that micro-channels or surface treatments are required to break droplets below the picoliter range, resulting in an increased experimental or chemical complexity. Herein, we study the spontaneous liquid film breakup observed after smearing a mother droplet on the surface of lotus leave reversal. As the film breaks and recedes, it is mechanically trapped in single wells formed by the natural bulges on the leaf and forms picoliter droplets, which we called anti-lotus effect droplet. The bio-inspired effect has the advantage of resisting capillary forces and releasing surface tension in the three-phase interface. Using soft-lithography and spin-coating, we obtain the bio-mimetic wells array with the corrugated surface analogous with the lotus leave, which enables a deeper liquid penetration during the smearing step that results in a more efficient filling of the wells. In the present experiments, we have obtained a regular array of 2 pL droplets with a density of 62,500 droplets/cm 2 , which equivalents to 5.4 million droplets on the surface a 96 well plate. This bio-inspired method of forming droplet will be a convenient way to operate with no chemical treatment and seal. It undoubtedly decreases the complexity of chip manufacturing and potential interference by chemical treatments. We believe that such a method contributes to the development of the efficient micro-droplet generator with a pure physical interface and thus is applicable to biological and medical analysis.

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