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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

I am a PhD student in DTLAB in KAIST, EE, advised by professor Youngsoo Shin. I received B.S. degree from KAIST EE in 2018 and expected to get PhD degree in Feb. 2023. My main research area is machine learning-guided physical design and computational lithography.

I can describe myself as a full-stack CAD engineer since I have research experience from RTL-level to physical design flow, as well as mask synthesis techniques. I especially worked on using ML to accelerate CAD flow and use as optimization guide.

I am looking for a job position for next year (available from Mar. 2023).

PDF version of this resume can be found here.

Education

Research intern

Aug. 2022 - Oct. 2022
Siemens EDA

Conducted research in machine learning-guided OPC in Calibre group.

PhD in School of Electrical Engineering

2019 - Present (Expected Feb. 2023)
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

  • [C15] Multisource clock tree synthesis through sink clustering and fast clock latency prediction (submitted)
    • Byungho Choi, Yonghwi Kwon, Umar Afzaal, and Youngsoo Shin
    IEEE International Symposium on Circuits and Systems (ISCAS), 2023
  • [C14] Fast prediction of dynamic IR-Drop using recurrent U-net architecture
    • Yonghwi Kwon and Youngsoo Shin
    ACM/IEEE Workshop on Machine Learning for CAD (MLCAD), Sep. 2022
  • [J4] Calibration of Compact Resist Model Through CNN Training (in revision)
    • Yonghwi Kwon and Youngsoo Shin
    IEEE Transactions on Semiconductor Manufacturing (TSM)
  • [P1] METHOD AND APPARATUS FOR PREDICTING VOLTAGE DROP IN SEMICONDUCTOR DEVICE
    • Youngsoo Shin, Yonghwi Kwon, and Giyoon Jung
    Korea Patent 10-2021-0184730
  • [J3] Integrated Test Pattern Extraction and Generation for Accurate Lithography Modeling
    • Gangmin Cho, Yonghwi Kwon, Pervaiz Kareem, and Youngsoo Shin
    IEEE Transactions on Semiconductor Manufacturing (TSM), vol. 35, Aug. 2022
  • [C13] Context-aware fast optical proximity correction using bidirectional recurrent neural network
    • Yonghwi Kwon and Youngsoo Shin
    SPIE Advanced Lithography, Apr. 2022
  • [C12] Refragmentation through machine learning classifier for fast optical proximity correction
    • Gangmin Cho, Byungho Choi, Yonghwi Kwon, and Youngsoo Shin
    SPIE Advanced Lithography, Apr. 2022
  • [C11] Synthesis of hotspot patterns using generative network trained with hotspot probability
    • Byungho Choi, Gangmin Cho, Yonghwi Kwon, and Youngsoo Shin
    SPIE Advanced Lithography, Apr. 2022
  • [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 (2021 IEEE TSM Best Paper Award)
    • 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

    Reviewer of IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
    Jul. 2022 ~
    IEEE Transactions on Semiconductor Manufacturing (TSM) 2021 Best Paper Award
    Feb. 2022 (see TSM May 2022 vol.35 Editoral)
    Recipient of 2022 SPIE Nick Cobb Memorial Scholarship
    Jan. 2022, $10,000 scholarship (see SPIE News)
    Young Fellow @ 58th Design Automation Conf. (DAC)
    Dec. 2021
    Best paper award nomination @ ACM Great Lakes Symposium on VLSI (GLSVLSI)
    Aug. 2020
    Invited speaker @ Optical Society Korea (OSK) Summer Meeting
    Jul. 2020
    Second prize @ SK Hynix Open Idea Contest
    Nov. 2019, received with 18,000$ reward cash (see SK hynix Newsroom, in Korean)
    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, C++

    Verilog

    Tool proficiency

    TensorFlow, PyTorch

    Synopsys Proteus & S-litho

    Synopsys ICC2 & DC & PT

    Siemens SI Calibre

    HSPICE

    Ansys RedHawk