Career Profile
Yonghwi Kwon is currently a PhD student in School of Electrical Engineering, KAIST, Korea. Under supervision of Prof. Youngsoo Shin, his main research area is machine learning guided physical design, computational lithography, and low power design.
Education
MS & PhD integrated program. KAIST EE has ranked 17th place in 2020 QS department rankings (top in domestic).
Projects
Dynamic voltage drop prediction with CNN (2019 - present)
Graph neural network guided leakage optimization (2019 - present)
Gate-level clock gating for cyclic logic paths (2019 - present)
RNN based OPC acceleration (2018 - 2020)
Synthetic test pattern generation using generative model (2018 - 2020)
Machine learning based SRAF printing prediction (2018 - 2019)
Early stage clock network power estimation (2017 - 2019)
Publications
IEEE/ACM Design Automation Conference (DAC), 2021
IEEE International Symposium on Circuits and Systems (ISCAS), 2021
SPIE Advanced Lithography, 2021
SPIE Advanced Lithography, 2021
SPIE Advanced Lithography, 2021
IEEE Transactions on Semiconductor Manufacturing (TSM)
ACM Great Lakes Symposium on VLSI (GLSVLSI), Aug. 2020
ACM/IEEE International Symposium on Low Power Electronics and Design (ISLPED), Sep. 2020
IEEE Transactions on Semiconductor Manufacturing (TSM), vol. 33, May 2020
SPIE Advanced Lithography, Feb. 2020
IEEE/ACM International Conference on Computer-Aided Design (ICCAD), Nov. 2019
SPIE Advanced Lithography, Feb. 2019
IEEE International Symposium on Circuits and Systems (ISCAS), May 2018
Honors
Best paper award nomination @ ACM Great Lakes Symposium on VLSI (GLSVLSI)
Aug. 2020
Aug. 2020
Invited speaker @ Optical Society Korea (OSK) Summer Meeting
Jul. 2020
Jul. 2020
Second prize @ SK Hynix Open Idea Contest
Nov. 2019, received with 18,000$ reward cash.
Nov. 2019, received with 18,000$ reward cash.
Invited speaker @ Next Generation Lithography (NGL) conf.
Aug. 2019
Aug. 2019
Richard Newton Young Fellow @ 56th Design Automation Conf. (DAC)
Jun. 2019
Jun. 2019
Volunteer Acitivty
Program management for 80 students and 12 mentors.
Bi-weekly teaching science and mathmatics to underpriviledged student group with talent on science. (Funded by Ministry of Science and ICT)