Taeyoung Kim (김태영)

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. (Sep. 2023 – Present)
  • KAIST, School of Electrical Engineering M.S. (Sep. 2021 – Aug. 2023)
  • Korea University, School of Electrical Engineering B.S. (Mar. 2015 – Aug. 2021)
  • Incheon Science High School (Mar. 2013 – Feb. 2015)

Honors

Design methodology for advanced technology,  GPU acceleration for CAD algorithm

Research Interests

  • Machine learning based computational lithography
  • Reinforcement learning for EDA

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. Daijoon Hyun, Sunwha Koh, Younggwang Jung, Taeyoung Kim, and Youngsoo Shin, “Routability optimization of extreme aspect ratio design through non-uniform placement utilization and selective flip-flop stacking,” ACM Transactions on Design Automation of Electronic Systems, vol. 28, no. 4, pp. 50:1-50:19, May 2023.

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, Feb. 2024.
  2. 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.
  3. 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.
  4. Taeyoung Kim, Gangmin Cho, and Youngsoo Shin, “Optical proximity correction with PID control through reinforcement learning,” Proc. SPIE Advanced Lithography, Feb. 2023.
  5. 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.