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Engineering and Technology

D-Index
43
Citations
6879
World Ranking
6213
National Ranking
1174

Overview

Bei Yu is affiliated with the Chinese University of Hong Kong in China and has a substantial body of research primarily within computer science and engineering. Their work encompasses several subfields including electrical and electronic engineering, computer vision and pattern recognition, artificial intelligence, hardware and architecture, and industrial and manufacturing engineering.

Their research topics cover a diverse range of areas such as advancements in photolithography techniques, VLSI and FPGA design techniques, advanced neural network applications, industrial vision systems and defect detection, domain adaptation and few-shot learning, VLSI and analog circuit testing, as well as parallel computing and optimization techniques.

Bei Yu has published extensively, contributing to several frequent publication venues:

  • IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
  • arXiv (Cornell University)
  • ACM Transactions on Design Automation of Electronic Systems
  • 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD)
  • Proceedings of the AAAI Conference on Artificial Intelligence

Key recent papers authored or coauthored by Bei Yu include:

  • Machine Learning for Electronic Design Automation: A Survey, 2021, ACM Transactions on Design Automation of Electronic Systems
  • PCL: Proxy-based Contrastive Learning for Domain Generalization, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Seeing Dynamic Scene in the Dark: A High-Quality Video Dataset with Mechatronic Alignment, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • MLCAD: A Survey of Research in Machine Learning for CAD Keynote Paper, 2021, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
  • BOOM-Explorer: RISC-V BOOM Microarchitecture Design Space Exploration Framework, 2021, 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD)

Frequent collaborators include Yuzhe Ma, Qi Sun, Tinghuan Chen, Martin D. F. Wong, and Tsung-Yi Ho, reflecting a collaborative approach across various aspects of computer-aided design and machine learning.

Best Publications

  • Recent advances in convolutional neural network acceleration

    Qianru Zhang;Meng Zhang;Tinghuan Chen;Tinghuan Chen;Zhifei Sun

  • Machine Learning for Electronic Design Automation: A Survey

    Guyue Huang;Jingbo Hu;Yifan He;Jialong Liu

  • Provably Secure Camouflaging Strategy for IC Protection

    Meng Li;Kaveh Shamsi;Travis Meade;Zheng Zhao

  • Layout Decomposition for Triple Patterning Lithography

    Bei Yu;Kun Yuan;Duo Ding;David Z. Pan

  • Self-Aligned Double Patterning Aware Pin Access and Standard Cell Layout Co-Optimization

    Xiaoqing Xu;Brian Cline;Greg Yeric;Bei Yu

  • Layout decomposition for triple patterning lithography

    Bei Yu;Kun Yuan;Boyang Zhang;Duo Ding

  • Optical proximity correction with hierarchical Bayes model

    Tetsuaki Matsunawa;Bei Yu;David Z. Pan

  • DeepBillboard: systematic physical-world testing of autonomous driving systems

    Husheng Zhou;Wei Li;Zelun Kong;Junfeng Guo

  • Layout Hotspot Detection With Feature Tensor Generation and Deep Biased Learning

    Haoyu Yang;Jing Su;Yi Zou;Yuzhe Ma

  • GAN-OPC: Mask Optimization With Lithography-Guided Generative Adversarial Nets

    Haoyu Yang;Shuhe Li;Zihao Deng;Yuzhe Ma

  • PARR: Pin-Access Planning and Regular Routing for Self-Aligned Double Patterning

    Xiaoqing Xu;Bei Yu;Jhih-Rong Gao;Che-Lun Hsu

  • Imbalance aware lithography hotspot detection: a deep learning approach

    Haoyu Yang;Luyang Luo;Jing Su;Chenxi Lin

  • Layout Hotspot Detection with Feature Tensor Generation and Deep Biased Learning

    Haoyu Yang;Jing Su;Yi Zou;Bei Yu

  • Design for Manufacturing With Emerging Nanolithography

    D. Z. Pan;Bei Yu;Jhih-Rong Gao

  • High Performance Graph ConvolutionaI Networks with Applications in Testability Analysis

    Yuzhe Ma;Haoxing Ren;Brucek Khailany;Harbinder Sikka

  • Enabling online learning in lithography hotspot detection with information-theoretic feature optimization

    Hang Zhang;Bei Yu;Evangeline F. Y. Young

  • Seeing Dynamic Scene in the Dark: A High-Quality Video Dataset With Mechatronic Alignment

    Ruixing Wang;Xiaogang Xu;Chi-Wing Fu;Jiangbo Lu

  • EPIC: Efficient prediction of IC manufacturing hotspots with a unified meta-classification formulation

    Duo Ding;Bei Yu;Joydeep Ghosh;David Z. Pan

  • A new lithography hotspot detection framework based on AdaBoost classifier and simplified feature extraction

    Tetsuaki Matsunawa;Jhih Rong Gao;Bei Yu;David Z. Pan

  • MLCAD: A Survey of Research in Machine Learning for CAD Keynote Paper

    Martin Rapp;Hussam Amrouch;Yibo Lin;Bei Yu

  • GAN-OPC: mask optimization with lithography-guided generative adversarial nets

    Haoyu Yang;Shuhe Li;Yuzhe Ma;Bei Yu

  • MOSAIC: Mask Optimizing Solution With Process Window Aware Inverse Correction

    Jhih-Rong Gao;Xiaoqing Xu;Bei Yu;David Z. Pan

Frequent Co-Authors

David Z. Pan
David Z. Pan The University of Texas at Austin
Evangeline F. Y. Young
Evangeline F. Y. Young Chinese University of Hong Kong
Derong Liu
Derong Liu University of Illinois at Chicago
Charles J. Alpert
Charles J. Alpert Cadence Design Systems
Jiaya Jia
Jiaya Jia Hong Kong University of Science and Technology
Ozgur Sinanoglu
Ozgur Sinanoglu New York University Abu Dhabi
Ulf Schlichtmann
Ulf Schlichtmann Technical University of Munich
Lars W. Liebmann
Lars W. Liebmann Intel (United States)
Ross Baldick
Ross Baldick The University of Texas at Austin
Yier Jin
Yier Jin University of Florida

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