FAMILY

Design Technology Lab

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

  • Lithography
  • Optical proximity correction (OPC)
  • Machine learning

Honors

  1. Young Fellow Award @ 58th DAC (Dec. 2021)

Publications

Journal Papers

  1. 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.

Conference Papers

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.