
Yonghwi Kwon (권용휘)
Ph.D. candidate, School of EE, KAIST
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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. (M.S. integrated) (Mar. 2019 – Present)
- KAIST, School of Electrical Engineering M.S. (Mar. 2018 – Feb. 2019)
- KAIST, School of Electrical Engineering B.S. (Mar. 2014 – Feb. 2018)
- Daegu Science High school (Mar. 2011 – Feb. 2014)
Research Interests
- Machine learning guided computational lithography
- Clock power optimization and estimation
Honors
- Nick cobb memorial scholarship @ 2022 SPIE (Jan. 2022, see SPIE News)
- Young Fellow Award @ 58th DAC (Dec. 2021)
- Best paper award @ 2021 IEEE T-SM (May. 2021, see IEEE TSM May 2022 Editorial)
- Nominated for best paper award @ 2020 Great Lakes Symp. on VLSI (May. 2020)
- Excellence award (with ~18k$ funding) @ SK Hynix Open Idea Contest (Nov. 2019, see SK hynix Newsroom)
- Invited talk @ Next Generation Lithography (NGL) conference 2019 (Aug. 2019)
- Richard Newton Young Fellow Award @ 56th DAC (June. 2019)
Publications
Journal Papers
- Yonghwi Kwon and Youngsoo Shin, “Calibration of compact resist model through CNN training,” IEEE Transactions on Semiconductor Manufacturing, (TSM) (under review)
- 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.
- Yonghwi Kwon and Youngsoo Shin, “Optical proximity correction using bidirectional recurrent neural network with attention mechanism,” IEEE Transactions on Semiconductor Manufacturing, vol. 34, no. 2, pp. 168-176, May. 2021. (Best paper award)
- Pervaiz Kareem, Yonghwi Kwon, and Youngsoo Shin, “Layout pattern synthesis for lithography optimizations,” IEEE Transactions on Semiconductor Manufacturing, vol. 33, no. 2, pp. 283-290, May. 2020.
Conference Papers
- Yonghwi Kwon and Youngsoo Shin, “Matrix-OPC with fast MEEF prediction using artificial neural network,” Proc. SPIE Advanced Lithography, Apr. 2022.
- Gangmin Cho, Byungho Choi, Yonghwi Kwon, and Youngsoo Shin, “Refragmentation through machine learning classifier for fast optical proximity correction,” Proc. SPIE Advanced Lithography, Apr. 2022.
- Byungho Choi, Yonghwi Kwon, Gangmin Cho, and Youngsoo Shin, “Synthesis of hotspot patterns using generative network trained with hotspot probability,” Proc. SPIE Advanced Lithography, Apr. 2022.
- Yonghwi Kwon, Daijoon Hyun, Giyoon Jung, and Youngsoo Shin, “Dynamic IR drop prediction using image-to-image translation neural network,” Proc. Int’l Symp. on Circuits and Systems (ISCAS), May 2021.
- Yonghwi Kwon, and Youngsoo Shin, “Optimization of accurate resist kernels through convolutional neural network,” 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.
- 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.
- Sunwha Koh, Yonghwi Kwon, and Youngsoo Shin, “Pre-layout clock tree estimation and optimization using artificial neural network,” Proc. Int’l Symp. on Low Power Electronics and Design (ISLPED), Aug. 2020.
- Wonjae Lee, Yonghwi Kwon, and Youngsoo Shin, “Fast ECO leakage optimization using graph convolutional network,” Proc. Great Lakes Symp. on VLSI (GLSVLSI), May. 2020. (Best paper award candidate)
- Yonghwi Kwon, Jinho Yang, Sungho Kim, Cheolkyun Kim, and Youngsoo Shin, “SRAF printing prediction using artificial neural network,” Proc. SPIE Advanced Lithography, Feb. 2020.
- Yonghwi Kwon, Inhak Han and Youngsoo Shin, “Clock gating synthesis of netlist with cyclic logic paths,” Proc. Int’l Conf. on Computer-Aided Design (ICCAD), Nov. 2019
- Yonghwi Kwon, Youngsoo Song, and Youngsoo Shin, “Optical proximity correction using bidirectional recurrent neural network (BRNN),” Proc. SPIE Advanced Lithography, pp. 109620D-1-109620D-8, Feb. 2019.
- Yonghwi Kwon, Jinwook Jung, Inhak Han, and Youngsoo Shin, “Transient clock power estimation of pre-CTS netlist,” Proc. Int’l Symp. on Circuits and Systems (ISCAS), pp.1-4, May. 2018.