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作者： Zhu, Ziran Chen, Jianli Zhu, Wenxing Chang, Yao-Wen Center for Discrete Mathematics and Theoretical Computer Science Fuzhou University Fuzhou China State Key Laboratory of ASIC and System Fudan University Shanghai China Department of Electrical Engineering Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan

Mixed-cell-height circuits have become popular in advanced technologies for better power, area, routability, and performance tradeoffs. With technology and region constraints imposed by modern circuit designs, the mixed-cell-height legalization problem has become even more challenging. Additionally, an ideal legalization method should minimize both the average and maximum cell movements to preserve the quality of a given placement as much as possible. In this article, we present an effective and efficient mixed-cell-height legalization algorithm to consider technology and region constraints while minimizing the average and maximum cell movements. We first present a fence region handling technique to unify the fence regions and the default region. To obtain a desired cell assignment, we then propose a movement-aware cell reassignment method by iteratively reassigning cells in locally dense areas to their desired rows. After cell reassignment, a technology-aware legalization is presented to remove cell overlaps while satisfying the technology constraints. Finally, we propose a technology-aware refinement to further reduce the average and maximum cell movements without increasing the technology constraints violations. Compared with the champion of the 2017 CAD Contest at ICCAD and the state-of-the-art work, experimental results based on the 2017 CAD Contest at ICCAD benchmarks show that our algorithm achieves the best average and maximum cell movements and significantly fewer technology constraint violations, in a comparable runtime. The experimental results based on the modified 2015 ISPD Contest benchmarks also demonstrate the effectiveness of our algorithm in minimizing the average and maximum cell movements, compared with state-of-the-art mixed-cell-height legalizers. © 1982-2012 IEEE.

关键词： Iterative methods

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An Efficient Memory Partitioning Approach for Multi-Pattern Data Access in STT-RAM

An Efficient Memory Partitioning Approach for Multi-Pattern ...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Binbin Liu Fan Yang Dian Zhou Xuan Zeng State Key Lab of ASIC & System School of Microelectronics 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

This paper presents an efficient memory partitioning algorithm for multi-pattern data access in Spintransfer torque random access memory (STT-RAM). The algorithm generates check patterns to solve read disturbance problem while ensuring multi-pattern data access in parallel. The implementation results imply that our proposed approach highly outperforms state-of-the-art algorithm in terms of bank number, searching time, performance and storage overhead.

关键词： Partitioning algorithms Arrays Memory management Random access memory Schedules Field programmable gate arrays Torque

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Dynamic neural network with memristive CIM and CAM for 2D and 3D vision

arXiv

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arXiv 2024年

作者： Zhang, Yue Zhang, Woyu Wang, Shaocong Lin, Ning Yu, Yifei He, Yangu Wang, Bo Jiang, Hao Lin, Peng Xu, Xiaoxin Qi, Xiaojuan Wang, Zhongrui Zhang, Xumeng Shang, Dashan Liu, Qi Cheng, Kwang-Ting Liu, Ming Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Key Laboratory of Microelectronic Devices & Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing100049 China ACCESS – AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong Institute of the Mind The University of Hong Kong Hong Kong Key Lab of Fabrication Technologies for Integrated Circuits Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Integrated Chips and Systems Frontier Institute of Chip and System Fudan University Shanghai200433 China University of Chinese Academy of Sciences Beijing100049 China College of Computer Science and Technology Zhejiang University Zhejiang310027 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

The brain is dynamic, associative and efficient. It reconfigures by associating the inputs with past experiences, with fused memory and processing. In contrast, AI models are static, unable to associate inputs with past experiences, and run on digital computers with physically separated memory and processing. We propose a hardware-software co-design, a semantic memory-based dynamic neural network (DNN) using memristor. The network associates incoming data with the past experience stored as semantic vectors. The network and the semantic memory are physically implemented on noise-robust ternary memristor-based Computing-In-Memory (CIM) and Content-Addressable Memory (CAM) circuits, respectively. We validate our co-designs, using a 40nm memristor macro, on ResNet and PointNet++ for classifying images and 3D points from the MNIST and ModelNet datasets, which not only achieves accuracy on par with software but also a 48.1% and 15.9% reduction in computational budget. Moreover, it delivers a 77.6% and 93.3% reduction in energy consumption. © 2024, CC BY.

关键词： Memristors

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Near-Infrared Heterojunction Field Modulated Phototransistors with Distinct Photodetection/Photostorage Switching Feature for Artificial Visuals

Research Square

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Research Square 2021年

作者： Han, Jiayue Du, Xiaoyang Zhang, Zhenghan He, Zeyu Xie, Runzhang Shan, Chongxin Tao, Silu Hu, Weida Yang, Ming Gou, Jun Wu, Zhiming Jiang, Yadong Wang, Jun School of Optoelectronic Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China State Key Laboratory of ASIC and System School of Microelectronics Fudan University Shanghai China State Key Laboratory of Infrared Physics Shanghai Institute of Technical Physics Chinese Academy of Science 500 Yutian Road Shanghai200083 China Henan Key Laboratory of Diamond Optoelectronic Materials and Devices School of Physics and Engineering Zhengzhou University Zhengzhou450001 China State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu610054 China

With the rising demand of recording, computing and image capture, advanced optoelectronic detection, storage and logic devices are highly pursued. Nevertheless, multi-functional vision chip based on infrared detection and memory switching has never been demonstrated. Here, by utilizing the electronic extraction layer ZnO and face-on orientation of D-A, we exhibit the broadband visible to near-infrared photo-response and photo-storage characters on graphene phototransistor. Functions as photodetection and photo-storage can be switched with the variation of gate voltage. The device demonstrates high photo-responsivity up to 1.88×106 A/W at 895 nm corresponding detectivity of 4.8×1012 Jones. Importantly, the rewritable and switching infrared optoelectronic memory function can be achieved with good retention over 104 s. The both retinomorphic vision and memorial preprocessing in artificial visual are simultaneously realized by photodetection/photostorage switching property. Such nearly all-solution processes in our phototransistors may open up the path for the large-scale and easy manufacturing infrared multifunctional bio-optoelectronic device. © 2021, CC BY.

关键词： Graphene

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Recent progress of integrated circuits and optoelectronic chips

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Science China(Information Sciences) 2021年第10期64卷 103-135页

作者： Yue HAO Shuiying XIANG Genquan HAN Jincheng ZHANG Xiaohua MA Zhangming ZHU Xingxing GUO Yahui ZHANG Yanan HAN Ziwei SONG Yan LIU Ling YANG Hong ZHOU Jiangyi SHI Wei ZHANG Min XU Weisheng ZHAO Biao PAN Yangqi HUANG Qi LIU Yimao CAI Jian ZHU Xin OU Tiangui YOU Huaqiang WU Bin GAO Zhiyong ZHANG Guoping GUO Yonghua CHEN Yong LIU Xiangfei CHEN Chunlai XUE Xingjun WANG Lixia ZHAO Xihua ZOU Lianshan YAN Ming LI School of Microelectronics Xidian University State Key Laboratory of Integrated Service Networks Xidian University National Integrated Circuit Innovation Center School of Microelectronics Fudan University School of Integrated Circuit Science and Engineering Beihang University China Center for Information Industry Development Frontier Institute of Chip and System Fudan University Institute of Microelectronics Peking University Nanjing Electronic Devices Institute State Key Laboratory of Functional Material for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Institute of Microelectronics Tsinghua University Center for Carbon-based Electronics Department of Electronics Peking University CAS Key Laboratory of Quantum Information University of Science and Technology of China CAS Center for Excellence and Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China Origin Quantum Computing Company Limited Key Laboratory of Flexible Electronics & Institute of Advanced Materials Nanjing Tech University State Key Laboratory of Electronic Thin Films and Integrated Devices School of Optoelectronic Science and EngineeringUniversity of Electronic Science and Technology of China National Laboratory of Solid State Microstructures & College of Engineering and Applied Sciences Nanjing University State Key Laboratory on Integrated Optoelectronics Institute of Semiconductors Chinese Academy of Sciences Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University School of Electrical and Electronic Engineering Tiangong University Center for Information Photonics and Communications School of Information Science and TechnologySouthwest Jiaotong University School of Electronic Electrical and Communication Engineering

Integrated circuits(ICs) and optoelectronic chips are the foundation stones of the modern information society. The IC industry has been driven by the so-called "Moore's law" in the past 60 years, and now has entered the post Moore's law era. In this paper, we review the recent progress of ICs and optoelectronic chips. The research status, technical challenges and development trend of devices, chips and integrated technologies of typical IC and optoelectronic chips are focused on. The main contents include the development law of IC and optoelectronic chip technology, the IC design and processing technology, emerging memory and chip architecture, brain-like chip structure and its mechanism, heterogeneous integration, quantum chip technology, silicon photonics chip technology, integrated microwave photonic chip, and optoelectronic hybrid integrated chip.

关键词： integrated circuit semiconductor science and technology optoelectronic device and chip photonic integrated circuit wide bandgap semiconductors silicon photonics hybrid integration quantum chip integrated microwave photonic photonic neural computing

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Unijunction Transistor on Silicon-On-Insulator Substrate 15

Unijunction Transistor on Silicon-On-Insulator Substrate

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15th IEEE International Conference on Solid-state and Integrated Circuit Technology, ICSICT 2020

作者： Chen, Y.X. Liu, J. Xiao, K. Zaslavsky, A. Cristoloveanu, S. Liu, F.Y. Li, B. Wan, J. State Key Lab of ASIC and System School of Information Science and Engineering Fudan University Shanghai China Brown University Department of Physics and School of Engineering ProvidenceRI02912 United States IMEP-LAHC INP-Grenoble/Minatec CS 50257 Grenoble38016 France Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China

ISBN: (纸本)9781728162355

A unijunction transistor based on fully-depleted silicon-on-insulator substrate is proposed. The device structure is similar to a junction field effect transistor. By conducting the TCAD simulation, we observe sharp switching and large hysteresis in emitter current-emitter voltage curves with the turn-on voltage linearly controlled by the second base voltage. The operation of the device is mainly determined by the emitter-channel PN junction, which is induced by the backgate voltage. The impact of the backgate voltage on the electrical characteristics is analyzed by changes in the channel potential. © 2020 IEEE.

关键词： Silicon on insulator technology

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A Machine-Learning-Resistant 3D PUF with 8-layer Stacking Vertical RRAM and 0.014% Bit Error Rate Using In-Cell Stabilization Scheme for IoT Security Applications

A Machine-Learning-Resistant 3D PUF with 8-layer Stacking Ve...

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International Electron Devices Meeting (IEDM)

作者： Jianguo Yang Dengyun Lei Deyang Chen Jing Li Haijun Jiang Qingting Ding Qing Luo Xiaoyong Xue Hangbing Lv Xiaoyang Zeng Ming Liu Zhejiang Lab Hangzhou China Key Laboratory of Microelectronics Devices and Integrated Technology Institute of Microelectronics of the Chinese Academy of Sciences Beijing China The No.5 Electronics Research Institute of the Ministry of Industry and Information Technology Guangzhou China State Key Laboratory of ASIC and System School of Microelectronics Fudan University Shanghai China

ISBN: (数字)9781728188881

ISBN: (纸本)9781728188898

In this work, we propose and demonstrate a multi-layer 3-dimensional (3D) vertical RRAM (VRRAM) PUF with in-cell stabilization scheme to improve both cost efficiency and reliability. An 8-layer VRRAM array was manufactured with excellent uniformity and good endurance of >10 7 . Apart from the variation in RRAM resistance, enhanced randomness is obtained thanks to the parasitic IR drop and abundant sneak current paths in 3D VRRAM. To deal with the common issue of unstable bits in PUF output, in-cell stabilization is proposed by first employing asymmetric biasing to detect the unstable bits and then exploiting reprogramming to expand the deviation to stabilize the output. The bit error rate is reduced by >7X (68X) for 3(5) times reprogramming. The proposed PUF features excellent resistance against machine learning attack and passes both National Institute of Standards and Technology (NIST) 800-22 and NIST 800-90B test suites.

关键词： Training Temperature measurement Three-dimensional displays NIST Tin Reliability Testing

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Two-Stage Neural Network Classifier for the Data Imbalance Problem with Application to Hotspot Detection 21

Two-Stage Neural Network Classifier for the Data Imbalance P...

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Proceedings of the 58th Annual ACM/IEEE Design Automation Conference

作者： Bingshu Wang Lanfan Jiang Wenxing Zhu Longkun Guo Jianli Chen Yao-Wen Chang Center for Discrete Mathematics and Theoretical Computer Science Fuzhou University Fuzhou China Center for Discrete Mathematics and Theoretical Computer Science Fuzhou University Fuzhou China and Peng Cheng Laboratory Shenzhen China State Key Lab of ASIC & System Fudan University Shanghai China Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan and Department of Electrical Engineering National Taiwan University Taipei Taiwan

ISBN: (纸本)9781665432740

The data imbalance problem often occurs in nanometer VLSI applications, where normal cases far outnumber error ones. Many imbalanced data handling methods have been proposed, such as oversampling minority class samples and downsampling majority class samples. However, existing methods focus on improving the quality of minority classes while causing quality deterioration of majority ones. In this paper, we propose a two-stage classifier to handle the data imbalance problem. We first develop an iterative neural network framework to reduce false alarms. Then the oversampling method on a final classification network is applied to predict the two classes better. As a result, the data imbalance problem is well handled, and the quality deterioration of majority classes is also reduced. Since the iterative stage does not change any existing network structure, any convolutional neural network can be used in the framework. Compared with the state-of-the-art imbalanced data handling methods, experimental results on the hotspot detection problem show that our two-stage classification method achieves the best prediction accuracy and reduces false alarms significantly.

关键词：

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Structural phase transition of monochalcogenides investigated with machine learning

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Physical Review B 2022年第9期105卷 094116-094116页

作者： J. Zhang F. Zhang D. Wei L. Liu X. Liu D. Fang G. X. Zhang X. Chen D. Wang School of Microelectronics & State Key Laboratory for Mechanical Behavior of Materials Xi'an Jiaotong University Xi'an 710049 China Key Lab of Micro-Nano Electronics and System Integration of Xi'an City Xi'an Jiaotong University Xi'an 710049 China Guangxi Key Laboratory of Optical and Electronic Materials and Devices College of Materials Science and Engineering Guilin University of Technology Guilin 541004 China Engineering Research Center of Nanoelectronic Integration and Advanced Equipment Ministry of Education School of Physics and Electronic Science East China Normal University Shanghai 200062 China MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter School of Physics Xi' an Jiaotong University Xi'an 710049 China School of Chemistry and Chemical Engineering Harbin Institute of Technology Harbin 150001 China Department of Applied Physics Aalto University Espoo 00076 Finland

As machine learning becomes increasingly important in science and engineering, it holds the promise to provide a universal approach applicable to various systems to investigate their crystalline phase transitions. Here, we build and train accurate artificial neural networks that can distinguish tiny energy difference, which is crucial to predict the crystalline phase transitions. Employing the trained artificial neural networks in Monte Carlo simulations as the surrogate energy function, we apply this approach to monochalcogenides, including bulk and two-dimensional monolayer SnTe and GeTe, investigating their crystalline phase transitions. The machine-learning approach, when viewed as providing a universal mathematical structure, can be transferred to the investigation of other materials when the training data set generated with ab initio methods are available. Moreover, the machine-learning approach resolves the difficulties associated with constructing the effective Hamiltonian for monochalcogenides, showing great potential with its accuracy and efficiency.

关键词： Ferroelectricity Structural phase transition Chalcogenides Machine learning

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A High Linearity T&H circuit for 1GHz Pipeline ADC in 65nm CMOS

A High Linearity T&H circuit for 1GHz Pipeline ADC in 65nm C...

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International Conference on Solid-state and Integrated Circuit Technology

作者： H. J. Wu Y. P. Zeng X. L. Xing N. Lin Q. Li Fudan Microelectronics Co. Ltd Shanghai China No.58 China Electronic Technology Group Corporation Wuxi China State Key Laboratory of ASIC and system Fudan University Shanghai China

ISBN: (数字)9781728162355

ISBN: (纸本)9781728162362

A 3.3/2.5V/1.2V high linearity track-and-hold (T&H) circuit for 1GHz Pipeline analog to digital converter (ADC) in 65nm CMOS is implemented. It includes the two input buffer with source follower, bias and common mode voltage and generated part, input-impedance matching network and SPI part. Input-impedance matching network include a 50- Ω /100- Ω /200-Ω resistor controlled with SPI. The simulated results show that the SFDR and SNDR of the input buffer can get 89dB and 88dB, respectively. The power consumption is 158mW with typical 3.3V/2.5V/1.2V supply.

关键词： Linearity Impedance matching Clocks Impedance Pipelines Generators Resistors

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