Gangmin Cho (조강민)

Ph.D. candidate, School of EE, KAIST

  • location
    291 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea
  • Office
    School of Electrical Engineering (E3-2), 5219

Education

  • KAIST, School of Electrical Engineering Ph.D. (Mar. 2021 – Present)
  • KAIST, School of Electrical Engineering M.S. (Feb. 2019 – Feb. 2021)
  • KAIST, School of Electrical Engineering B.S. (Mar. 2015 – Feb. 2019)
  • Gyeongsan Science High School (Mar. 2013 – Feb. 2015)

Research Interests

  • Machine learning guided computational lithography

Honors

  1. Best Paper Award – Honorable Mention @ 2022 IEEE T-SM (Aug. 2022, see IEEE TSM May 2023 Editorial)
  2. Best Student Paper Award @ 2022 Next Generation Lithography Conf.
  3. Young Fellow Award @ 58th DAC (Dec. 2021)

Publications

Journal Papers

  1. Gangmin Cho, Taeyoung Kim, and Youngsoo Shin, “Fast optical proximity correction using graph convolutional network with autoencoders,” IEEE Transactions on Semiconductor Manufacturing, vol. 36, issue 4, pp. 629-635, Nov. 2023.
  2. Gangmin Cho, Yonghwi Kwon, Pervaiz Kareem, and Youngsoo Shin, “Integrated test pattern extraction and generation for accurate lithography modeling,” IEEE Transactions on Semiconductor Manufacturing, vol. 35, no. 3, pp. 495-503, Jun. 2022. (Best Paper Award – Honorable Mention)

Conference Papers

  1. Gangmin Cho, Taeyoung Kim, Seohyun Kim, and Youngsoo Shin, “A fast and accurate PEB simulation through recurrent neural network,” Proc. SPIE Advanced Lithography, accepted.
  2. Seunggyu Lee, Daijoon Hyun, Younggwang Jung, Gangmin Cho, and Youngsoo Shin, “Fast IR-drop prediction of analog circuits using recurrent synchronous GCN and Y-net model,” Proc. Design, Automation & Test in Europe (DATE), accepted.
  3. Wonjae Lee, Insu Cho, Gangmin Cho, and Youngsoo Shin, “Routability-driven power distribution network synthesis with IR-drop budgeting,” Proc. Workshop on Machine Learning for CAD (MLCAD), Sep. 2023.
  4. Yoonsang Song, Gangmin Cho, Wonjae Lee, and Youngsoo Shin, “Simultaneous clock wire sizing and shield insertion for minimizing routing blockage,” Proc. Workshop on Machine Learning for CAD (MLCAD), Sep. 2023.
  5. Taeyoung Kim, Gangmin Cho, and Youngsoo Shin, “Block-level power net routing of analog circuit using reinforcement learning,” Proc. Int’l Symp. on Circuits and Systems (ISCAS), May 2023.
  6. Gangmin Cho, Taeyoung Kim, and Youngsoo Shin, “Fast and accurate prediction of process variation band with custom kernels extracted from convolutional networks,” Proc. SPIE Advanced Lithography, Feb. 2023.
  7. Taeyoung Kim, Gangmin Cho, and Youngsoo Shin, “Optical proximity correction with PID control through reinforcement learning,” Proc. SPIE Advanced Lithography, Feb. 2023.
  8. Gangmin Cho, Yonghwi Kwon, Taeyoung Kim, and Youngsoo Shin, “Refragmentation through machine learning classifier for fast and accurate optical proximity correction,” Proc. SPIE Advanced Lithography, Apr. 2022.
  9. Byungho Choi, Gangmin Cho, Yonghwi Kwon, and Youngsoo Shin, “Hotspot pattern synthesis using generative network with hotspot probability model,” Proc. SPIE Advanced Lithography, Apr. 2022.
  10. Gangmin Cho, Yonghwi Kwon, Pervaiz Kareem, Sungho Kim, and Youngsoo Shin, “Test pattern extraction for lithography modeling under design rule revisions,” Proc. SPIE Advanced Lithography, Feb. 2021.
  11. Pervaiz Kareem, Yonghwi Kwon, Gangmin Cho, and Youngsoo Shin, “Fast prediction of process variation band through machine learning models,” Proc. SPIE Advanced Lithography, Feb. 2021.
  12. Joonhyuk Cho, Gangmin Cho, and Youngsoo Shin, “Optimization of machine learning guided optical proximity correction,” Proc. Int’l Midwest Symp. on Circuits and Systems (MWSCAS), Aug. 2018.

Patents

  1. Gangmin Cho, Yonghwi Kwon, Taeyoung Kim, and Youngsoo Shin, “Machine learning-based refragmentation method and apparatus for optical proximity correction,” Korea patent 10-2022-0109643, Aug. 2022, submitted.