Publications

Summary: IEEE TCAD (48), DAC (26), ICCAD (33), etc.

Journal & Conference Papers

Accepted

2022

  • [C150] Binwu Zhu, Xinyun Zhang, Yibo Lin, Bei Yu, Martin Wong, “Efficient Design Rule Checking Script Generation via Key Information Extraction”, ACM/IEEE Workshop on Machine Learning for CAD (MLCAD), Snowbird, Utah, Sep. 12–13, 2022. (paper) (slides) (video) (Best Paper Award Nomination)

  • [C149] Zhuolun He, Yuzhe Ma, Bei Yu, “X-Check: GPU-Accelerated Design Rule Checking via Parallel Sweepline Algorithms”, IEEE/ACM International Conference on Computer-Aided Design (ICCAD), San Diego, Oct. 30–Nov. 3, 2022.

  • [C148] Wenqian Zhao, Xufeng Yao, Ziyang Yu, Guojin Chen, Yuzhe Ma, Bei Yu, Martin Wong, “AdaOPC: A Self-Adaptive Mask Optimization Framework For Real Design Patterns”, IEEE/ACM International Conference on Computer-Aided Design (ICCAD), San Diego, Oct. 30–Nov. 3, 2022.

  • [C147] Zhen Zhuang, Bei Yu, Kai-Yuan Chao, Tsung-Yi Ho, “Multi-Package Co-Design for Chiplet Integration”, IEEE/ACM International Conference on Computer-Aided Design (ICCAD), San Diego, Oct. 30–Nov. 3, 2022.

  • [C146] Liangjian Wen, Yi Zhu, Lei Ye, Guojin Chen, Bei Yu, Jianzhuang Liu, Chunjing Xu, “LayouTransformer: Generating Layout Patterns with Transformer via Sequential Pattern Modeling”, IEEE/ACM International Conference on Computer-Aided Design (ICCAD), San Diego, Oct. 30–Nov. 3, 2022.

  • [C145] Wanli Chen, Xinge Zhu, Guojin Chen, Bei Yu, “Efficient Point Cloud Analysis Using Hilbert Curve”, European Conference on Computer Vision (ECCV), Tel-Aviv, Oct. 23–27, 2022.

  • [C143] Qi Sun, Xinyun Zhang, Hao Geng, Yuxuan Zhao, Yang Bai, Haisheng Zheng, Bei Yu, “GTuner: Tuning DNN Computations on GPU via Graph Attention Network”, ACM/IEEE Design Automation Conference (DAC), San Francisco, CA, Jul. 10–14, 2022. (paper) (slides) (video)

  • [C142] Ziyi Wang, Chen Bai, Zhuolun He, Guangliang Zhang, Qiang Xu, Tsung-Yi Ho, Bei Yu, Yu Huang, “Functionality Matters in Netlist Representation Learning”, ACM/IEEE Design Automation Conference (DAC), San Francisco, CA, Jul. 10–14, 2022. (paper) (slides) (video)

  • [C141] Hao Geng, Qi Xu, Tsung-Yi Ho, Bei Yu, “PPATuner: Pareto-driven Tool Parameter Auto-tuning in Physical Design via Gaussian Process Transfer Learning”, ACM/IEEE Design Automation Conference (DAC), San Francisco, CA, Jul. 10–14, 2022. (paper) (slides) (video)

  • [C140] Mingjun Li, Jianlei Yang, Yingjie Qi, Meng Dong, Yuhao Yang, Runze Liu, Weitao Pan, Bei Yu, Weisheng Zhao, “Eventor: An Efficient Event-Based Monocular Multi-View Stereo Accelerator on FPGA Platform”, ACM/IEEE Design Automation Conference (DAC), San Francisco, CA, Jul. 10–14, 2022. (paper) (slides) (video)


  • [J64] Yibo Lin, Xiaohan Gao, Tinghuan Chen, Bei Yu, “Machine learning for digital circuit backend design”, Micro/nano Electronics and Intelligent Manufacturing, vol. 2, no. 3, 2022. (in Chinese) (paper)

  • [J61] Ran Chen, Wei Zhong, Haoyu Yang, Hao Geng, Fan Yang, Xuan Zeng, Bei Yu, “Faster Region-based Hotspot Detection”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 41, no. 3, pp. 669–680, 2022. (code) (paper)

2021

  • [C118] Tinghuan Chen, Qi Sun, Bei Yu, “Machine Learning in Nanometer AMS Design for Reliability”, IEEE International Conference on ASIC (ASICON), Kunming, China, Oct. 26–29, 2021. (paper) (slides) (Invited Paper)

  • [C116] Wei Li, Guojin Chen, Haoyu Yang, Ran Chen, Bei Yu, “Learning Point Clouds in EDA”, ACM International Symposium on Physical Design (ISPD), Mar. 21–Mar. 24, 2021. (paper) (slides) (Invited Paper)

  • [C111] Hongjia Li, Mengshu Sun, Tianyun Zhang, Olivia Chen, Nobuyuki Yoshikawa, Bei Yu, Yanzhi Wang, Yibo Lin, “Towards AQFP-Capable Physical Design Automation”, IEEE/ACM Proceedings Design, Automation and Test in Europe (DATE), Feb. 01–05, 2021. (paper)


  • [J58] Wei Li, Yuzhe Ma, Qi Sun, Lu Zhang, Yibo Lin, Iris Hui-Ru Jiang, Bei Yu, David Z. Pan, “OpenMPL: An Open Source Layout Decomposer”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 40, no. 11, pp. 2331–2344, 2021. (paper)

  • [J56] Guyue Huang, Jingbo Hu, Yifan He, Jialong Liu, Mingyuan Ma, Zhaoyang Shen, Juejian Wu, Yuanfan Xu, Hengrui Zhang, Kai Zhong, Xuefei Ning, Yuzhe Ma, Haoyu Yang, Bei Yu, Huazhong Yang, Yu Wang, “Machine Learning for Electronic Design Automation: A Survey”, ACM Transactions on Design Automation of Electronic Systems (TODAES), vol. 25, no. 5, 2021. (paper)

2020


2019

  • [C80] Bentian Jiang, Xiaopeng Zhang, Ran Chen, Gengjie Chen, Peishan Tu, Wei Li, Evangeline F. Y. Young, Bei Yu, “FIT: Fill Insertion Considering Timing”, ACM/IEEE Design Automation Conference (DAC), pp. 221:1–221:6, Las Vegas, NV, June 2–6, 2019. (paper) (slides) (poster)


2018


2017

  • [C57] Hang Zhang, Fengyuan Zhu, Haocheng Li, Evangeline F. Y. Young, Bei Yu, “Bilinear Lithography Hotspot Detection”, ACM International Symposium on Physical Design (ISPD), pp. 7–14, Portland, OR, Mar. 19–22, 2017. (paper) (Best Paper Award)


2016


2015


2014

2013


2012

  • [C13] Bei Yu, Jhih-Rong Gao, Duo Ding, Yongchan Ban, Jae-Seok Yang, Kun Yuan, Minsik Cho, David Z. Pan, “Dealing with IC Manufacturability in Extreme Scaling”, IEEE/ACM International Conference on Computer-Aided Design (ICCAD), pp. 240–242, San Jose, CA, Nov. 5–8, 2012. (paper) (Embedded Tutorial paper)


2011

2010

2009



Books / Book Chapters

 

[B4] Haoyu Yang, Yibo Lin, Bei Yu, “Machine Learning for Mask Synthesis and Verification”, in Machine Learning Applications in Electronic Design Automation, Mark Ren eds., Springer, 2022. (paper)

 

[B3] Shiyan Hu, Bei Yu, “Big Data Analytics for Cyber-Physical Systems”, Springer, 2020.

 

[B2] Bei Yu, David Z. Pan, “Design for Manufacturability with Advanced Lithography”, Springer, 2016.

 

[B1] Bei Yu, David Z. Pan, “Layout Decomposition for Triple Patterning”, in Encyclopedia of Algorithms, M.-Y. Kao eds., Springer, 2015. (paper)


Dissertation

Newsletters

  • [N2] Bei Yu, Gilda Garreton, David Z. Pan, “Layout Compliance for Triple Patterning Lithography: An Iterative Approach”, SPIE Newsroom.