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

Greetings! I’m currently an intern at NVIDIA research, working with Haoyu Yang and Mark Ren. I’m thrilled to have this opportunity to collaborate and expand my research horizons at NVIDIA research. I am also a final-year 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 representation 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.


2024.04 - Present

Logo  NVIDIA   Research Scientist Intern
Mentor & Manager. Haoyu Yang & Mark Ren Austin, TX

2023.08 - 2024.4

Logo  The University of Texas at Austin   Visiting Scholar
Supervisor : Prof. David Z. Pan Austin, TX

2018 - 2019

Logo  Tencent   Research Intern
Shenzhen, China


Representative publications that I am a primary author on are highlighted.
[Google Scholar; 174+ citations, h-index: 7+] [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


[P1] LLM-Enhanced Bayesian Optimization for Efficient Analog Layout Constraint Generation [bibtex] [paper] arXiv 2024


  • Apr. 29, 2024    Starting internship at NVIDIA Research. Let's rock'n'roll! Many thanks to my mentor Haoyu Yang and Mark Ren.
  • 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. 88 | 2024 TorchOPC/TorchLitho   Differentiable computational lithography with PyTorch
2. 92 | 2023 OpenOPC/OpenILT   Open-source inverse lithography technology (ILT) framework
3. 98 | 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 2024-06-16 , thanks to Bamos for the blog template.