Taeyoung Kim (김태영)
Ph.D. candidate, School of EE, KAIST
- Phone
- Email
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location291 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea
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OfficeSchool 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
- 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.