Gangmin Cho (조강민)
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. (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
- Young Fellow Award @ 58th DAC (Dec. 2021)
Publications
Journal Papers
- 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
- 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.
- 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.
- 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.
- 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.
- 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.