Members

Taeyoung Kim (김태영)

PH.D. Candidates, School of EE, KAIST

Office: Chungmugwan 501B, Department of Semiconductor Systems Engineering, Science and Technology, 209, Neungdong-ro, Gwangjin-gu, Seoul, Republic of Korea / School of Elerical Engineering (E3-2), 5219, KAIST

Personal Information

• Date of Birth : June 21th, 1997

• Place of Birth : Incheon, Korea

• Hobby : Visiting good restaurants

• E-mail : tykim21@kaist.ac.kr

Education

• Incheon Science High School (Mar. 2013 - Feb. 2015)

• Korea University, School of Electrical Engineering B.S. (Mar. 2015 - Aug. 2021)

• KAIST, School of Electrical Engineering M.S. (Sep. 2021 - Aug. 2023)

• KAIST, School of Electrical Engineering Ph.D. (Sep. 2023 - Present)

Journal Papers

• Taeyoung Kim, Shilong Zhang, and Youngsoo Shin, “Model-based OPC with adaptive PID control through reinforcement learning,” IEEE Transactions on Semiconductor Manufacturing, vol. 38, no. 1, pp. 48-56, Feb. 2025.

• 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.

• 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

• 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.

• 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.

• 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.

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

• 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.