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Yonghwi Kwon

PhD Student

Languages

  • Korean (Native)
  • English (Professional)

Interests

  • Machine learning guided physical design
  • Computational lithography
  • Low power design

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

PhD in School of Electrical Engineering

2019 - Present
Korea Advanced Institute of Science and Technology (KAIST)

MS & PhD integrated program. KAIST EE has ranked 17th place in 2020 QS department rankings (top in domestic).

MS in School of Electrical Engineering

2018 - 2019
Korea Advanced Institute of Science and Technology (KAIST)

BS in School of Electrical Engineering

2014 - 2018
Korea Advanced Institute of Science and Technology (KAIST)

Projects

CNN-based resist kernel optimization (2020 - present)
Graph neural network guided leakage optimization (2019 - 2021)
Gate-level clock gating for cyclic logic paths (2019 - present)
Dynamic voltage drop prediction with CNN (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

  • [C13] Context-aware fast optical proximity correction using bidirectional recurrent neural network (submitted)
    • Yonghwi Kwon and Youngsoo Shin
    SPIE Advanced Lithography, 2021
  • [C12] Refragmentation through machine learning classifier for fast optical proximity correction (submitted)
    • Gangmin Cho, Byungho Choi, Yonghwi Kwon, and Youngsoo Shin
    SPIE Advanced Lithography, 2021
  • [C11] Synthesis of hotspot patterns using generative network trained with hotspot probability (submitted)
    • Byungho Choi, Gangmin Cho, Yonghwi Kwon, and Youngsoo Shin
    SPIE Advanced Lithography, 2021
  • [C10] Dynamic IR drop prediction using image-to-image translation neural network
    • Yonghwi Kwon, Daijoon Hyun, Giyoon Jung, and Youngsoo Shin
    IEEE International Symposium on Circuits and Systems (ISCAS), May 2021
  • [J2] Optical proximity correction using bidirectional recurrent neural network with attention mechanism
    • Yonghwi Kwon and Youngsoo Shin
    IEEE Transactions on Semiconductor Manufacturing (TSM), vol. 34, May 2021
  • [C9] Test pattern extraction for lithography modeling under design rule revisions
    • Gangmin Cho, Yonghwi Kwon, Pervaiz Kareem, Sungho Kim, and Youngsoo Shin
    SPIE Advanced Lithography, Feb. 2021
  • [C8] Fast prediction of process variation band through machine learning models
    • Pervaiz Kareem, Yonghwi Kwon, Gangmin Cho, and Youngsoo Shin
    SPIE Advanced Lithography, Feb. 2021
  • [C7] Optimization of accurate resist kernels through convolutional neural network
    • Yonghwi Kwon and Youngsoo Shin
    SPIE Advanced Lithography, Feb. 2021
  • [C6] Fast ECO leakage optimization using graph convolutional network (Best paper award candidate)
    • Wonjae Lee, Yonghwi Kwon, and Youngsoo Shin
    ACM Great Lakes Symposium on VLSI (GLSVLSI), Aug. 2020
  • [C5] Pre-layout clock tree estimation and optimization using artificial neural network
    • Sunwha Koh, Yonghwi Kwon, and Youngsoo Shin
    ACM/IEEE International Symposium on Low Power Electronics and Design (ISLPED), Sep. 2020
  • [J1] Layout pattern synthesis for lithography optimizations
    • Pervaiz Kareem, Yonghwi Kwon, and Youngsoo Shin
    IEEE Transactions on Semiconductor Manufacturing (TSM), vol. 33, May 2020
  • [C4] SRAF printing prediction using artificial neural network
    • Yonghwi Kwon, Jinho Yang, Sungho Kim, Cheolkyun Kim, and Youngsoo Shin
    SPIE Advanced Lithography, Feb. 2020
  • [C3] Clock gating synthesis of netlist with cyclic logic paths
    • Yonghwi Kwon, Inhak Han, and Youngsoo Shin
    IEEE/ACM International Conference on Computer-Aided Design (ICCAD), Nov. 2019
  • [C2] Optical proximity correction using bidirectional recurrent neural network (BRNN)
    • Yonghwi Kwon, Youngsoo Song, and Youngsoo Shin
    SPIE Advanced Lithography, Feb. 2019
  • [C1] Transient clock power estimation of pre-CTS netlist
    • Yonghwi Kwon, Jinwook Jung, Inhak Han, and Youngsoo Shin
    IEEE International Symposium on Circuits and Systems (ISCAS), May 2018

    Honors

    Best paper award nomination @ ACM Great Lakes Symposium on VLSI (GLSVLSI)
    Aug. 2020
    Invited speaker @ Optical Society Korea (OSK) Summer Meeting
    Jul. 2020
    Second prize @ SK Hynix Open Idea Contest
    Nov. 2019, received with 18,000$ reward cash.
    Invited speaker @ Next Generation Lithography (NGL) conf.
    Aug. 2019
    Richard Newton Young Fellow @ 56th Design Automation Conf. (DAC)
    Jun. 2019

    Volunteer Acitivty

    Hanwha-KAIST Education Program for Gifted Students

    2019 - 2020
    Head mentor

    Program management for 80 students and 12 mentors.

    KAIST Science Outreach Program (KSOP)

    2015 - 2019
    Student mentor

    Bi-weekly teaching science and mathmatics to underpriviledged student group with talent on science. (Funded by Ministry of Science and ICT)

    Programming languages

    Python, Tcl/Tk, shell

    BASIC, MATLAB, C

    Verilog

    Tool proficiency

    TensorFlow, PyTorch

    Synopsys Proteus & S-litho

    Synopsys ICC & DC & PT

    Mentor Graphics Calibre