Do Not Go Gentle Into That Good Night.
--『 Interstellar 』, Dylan Thomas.

Greetings! I’m currently a visiting student at UT Austin, under the guidance of Professor David Z. Pan. I’m thrilled to have this opportunity to collaborate and expand my research horizons at UTDA. I am also a Ph.D. candidate at The Chinese University of Hong Kong (CUHK-CSE), where I have been fortunate to work with Professor Bei Yu and co-supervised by Professor Martin D. F. Wong.

Research Interests:

  • Scaling deep learning: large language models, LLM on EDA, large-scale layout representaion learning.
  • Design for manufacturing: computational lithography, mask optimization, OPC, SMO.
  • Optimization: bi-level & multi-level optimization, GPU acceleration, level-set optimization.
  • Deep learning in VLSI design: physics-informed networks for EDA problems

AI for EDA

I’m also maintaining a curated list of AI for EDA papers. Check it out here: Awesome AI for EDA.
The list is under construction and you are welcomed to submit your publications follow the instructions.


Representative publications that I am a primary author on are highlighted.
[Google Scholar; 137+ citations, h-index: 6+] [Download bibtex for all publications] [ORCID]

Conference papers

[C15] Efficient Bilevel Source Mask Optimization [bibtex] [paper]
Guojin Chen, Hongquan He, Peng Xu, Hao Geng, and Bei Yu
DAC 2024
[C14] Fracturing-aware Curvilinear ILT via Circular E-beam Mask Writer [bibtex]
Xinyun Zhang, Su Zheng, Guojin Chen, Binwu Zhu, Hong Xu, and Bei Yu
DAC 2024
[C13] Performance-driven Analog Routing via Heterogeneous 3DGNN and Potential Relaxation [bibtex]
Peng Xu, Guojin Chen, Keren Zhu, Tinghuan Chen, Tsung-Yi Ho, and Bei Yu
DAC 2024
[C12] Open-Source Differentiable Lithography Imaging Framework [bibtex] [paper] [code]
Guojin Chen, Hao Geng, Bei Yu, and David Z. Pan
SPIE 2024
[C11] AlphaSyn: Logic Synthesis Optimization with Efficient Monte Carlo Tree Search [bibtex] [paper]
Zehua Pei, Fangzhou Liu, Zhuolun He, Guojin Chen, Haisheng Zheng, Keren Zhu, and Bei Yu
ICCAD 2023
[C10] Physics-Informed Optical Kernel Regression Using Complex-valued Neural Fields [bibtex] [paper]
Guojin Chen, Zehua Pei, Haoyu Yang, Yuzhe Ma, Bei Yu, and Martin Wong
DAC 2023 (Best score in DFM track.)
[C9] DiffPattern: Layout Pattern Generation via Discrete Diffusion [bibtex] [paper]
Zixiao Wang, Yunheng Shen, Wenqian Zhao, Yang Bai, Guojin Chen, Farzan Farnia, and Bei Yu
DAC 2023
[C8] GPU-accelerated Matrix Cover Algorithm for Multiple Patterning Layout Decomposition [bibtex] [paper]
Guojin Chen, Haoyu Yang, and Bei Yu
SPIE 2023
[C7] Efficient Point Cloud Analysis Using Hilbert Curve. [bibtex] [paper]
Wanli Chen, Xinge Zhu, Guojin Chen, and Bei Yu
ECCV 2022
[C6] AdaOPC: A Self-Adaptive Mask Optimization Framework For Real Design Patterns [bibtex] [paper]
Wenqian Zhao, Xufeng Yao, Ziyang Yu, Guojin Chen, Yuzhe Ma, Bei Yu, and Martin Wong
ICCAD 2022
[C5] LayouTransformer: Generating Layout Patterns with Transformer via Sequential Pattern Modeling [bibtex] [paper]
Liangjian Wen, Yi Zhu, Lei Ye, Guojin Chen, Bei Yu, Jianzhuang Liu, and Chunjing Xu
ICCAD 2022
[C4] DevelSet: Deep Neural Level Set for Instant Mask optimization [bibtex] [abs] [paper]
Guojin Chen, Ziyang Yu, Hongduo Liu, Yuzhe Ma, and Bei Yu
ICCAD 2021
[C3] Learning Point Clouds in EDA. [bibtex] [abs] [paper]
Wei Li, Guojin Chen, Haoyu Yang, Ran Chen, and Bei Yu
ISPD 2021
[C2] DAMO: Deep Agile Mask Optimization for Full Chip Scale [bibtex] [abs] [paper]
Guojin Chen, Wanli Chen, Yuzhe Ma, Haoyu Yang, and Bei Yu
ICCAD 2020
[C1] A GPU-enabled Level Set Method for Mask Optimization [bibtex] [abs] [paper]
Ziyang Yu, Guojin Chen, Yuzhe Ma, and Bei Yu
DATE 2020

Journal papers

[J6] DeepOTF: Learning Equations-constrained Prediction for Electromagnetic Behavior [bibtex]
Peng Xu, Siyuan Xu, Tinghuan Chen, Guojin Chen, Tsung-Yi Ho, and Bei Yu
[J5] Ultra-Fast Source Mask Optimization via Conditional Discrete Diffusion [bibtex] [paper]
Guojin Chen, Zixiao Wang, Bei Yu, David Z. Pan, and Martin D.F. Wong
TCAD 2024
[J4] L2O-ILT: Learning to Optimize Inverse Lithography Techniques [bibtex] [paper]
Binwu Zhu, Su Zheng, Ziyang Yu, Guojin Chen, Yuzhe Ma, Fan Yang, Bei Yu, and Martin Wong
TCAD 2023
[J3] A GPU-Enabled Level-Set Method for Mask Optimization [bibtex] [paper]
Ziyang Yu, Guojin Chen, Yuzhe Ma, and Bei Yu
TCAD 2023
[J2] DevelSet: Deep Neural Level Set for Instant Mask optimization [bibtex] [paper]
Guojin Chen, Ziyang Yu, Hongduo Liu, Yuzhe Ma, and Bei Yu
TCAD 2023
[J1] DAMO: Deep Agile Mask Optimization for Full-Chip Scale [bibtex] [paper]
Guojin Chen, Wanli Chen, Qi Sun, Yuzhe Ma, Haoyu Yang, and Bei Yu
TCAD 2022


  • Feb. 25 2024    Traveling to SPIE24, San Jose. [slides], [codes]
  • Feb. 13, 2024    Three papers got accepted by DAC 2024.
  • Jan. 13, 2024    One TCAD about source mask optimization got accepted.
  • Aug. 01, 2023    Coming to Austin.
  • Jul. 28, 2023    Attending DAC conference.
  • Feb. 06, 2023    Two papers got accepted by DAC 2023, one paper get best score in DFM track.

Open Source Repositories

1. 50 | 2024 TorchOPC/TorchLitho   Differentiable computational lithography with PyTorch
2. 81 | 2023 OpenOPC/OpenILT   Open-source inverse lithography technology (ILT) framework
3. 87 | 2023 ai4eda/awesome-AI4EDA   A curated paper list of existing AI for EDA studies.

Professional Activities

Paper Review / External Review

2021-2023 Design Automation Conference (DAC)
2022-2023 AAAI Conference on Artificial Intelligence (AAAI)
2022-2023 IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD)


Python Computing (AIST 1110), TA F2022
Mobile Computing (CSCI 3310), TA S2022
Numerical Optimization (AIST 3010), TA F2021

Last updated on 2024-04-15 , thanks to Bamos for the blog template.