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DT LAB   →  Ph.D. Candidates   →  Yonghwi Kwon (권용휘)

Yonghwi Kwon (권용휘)

Ph.D. candidate, School of EE, KAIST
  • location
    291 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea
  • Office
    School 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

  1. Nick cobb memorial scholarship @ 2022 SPIE (Jan. 2022, see SPIE News)
  2. Young Fellow Award @ 58th DAC (Dec. 2021)
  3. Best paper award @ 2021 IEEE T-SM (May. 2021, see IEEE TSM May 2022 Editorial)
  4. Nominated for best paper award @ 2020 Great Lakes Symp. on VLSI (May. 2020)
  5. Excellence award (with ~18k$ funding) @ SK Hynix Open Idea Contest (Nov. 2019, see SK hynix Newsroom)
  6. Invited talk @ Next Generation Lithography (NGL) conference 2019 (Aug. 2019)
  7. Richard Newton Young Fellow Award @ 56th DAC (June. 2019)

Publications

Journal Papers

  1. Yonghwi Kwon and Youngsoo Shin, “Calibration of compact resist model through CNN training,” IEEE Transactions on Semiconductor Manufacturing, (TSM) (under review)
  2. 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.
  3. 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)
  4. 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

  1. Yonghwi Kwon and Youngsoo Shin, “Matrix-OPC with fast MEEF prediction using artificial neural network,” Proc. SPIE Advanced Lithography, Apr. 2022.
  2. 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.
  3. 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.
  4. 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.
  5. Yonghwi Kwon, and Youngsoo Shin, “Optimization of accurate resist kernels through convolutional neural network,” Proc. SPIE Advanced Lithography, Feb. 2021.
  6. 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.
  7. 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.
  8. 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.
  9. 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)
  10. Yonghwi Kwon, Jinho Yang, Sungho Kim, Cheolkyun Kim, and Youngsoo Shin, “SRAF printing prediction using artificial neural network,” Proc. SPIE Advanced Lithography, Feb. 2020.
  11. 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
  12. 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.
  13. 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.
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