Semiconductor Design Technology LAB Our research focus is in EDA (electronic design automation) and DT (design technology),
trying to revolutionize the way integrated circuits and systems are designed and manufactured.
 Some of our recent topics are 1. AI-EDA, 2. Lithography, 3. Low Power, 4. Exploratory Circuits

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RESEARCH TOPICS

AI-EDA
Lithography

Lithography

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Low Power

Low Power

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Exploratory Circuits

Exploratory Circuits

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LATEST NEWS & PUBLICATIONS

NEWS

  • Byungho Choi, Gangmin Cho, Yonghwi Kwon and Professor Shin received the “Best Student Paper Awards” from Next Generation Lithography Conference 2022
  • Yonghwi Kwon and Professor Shin received the “Best Paper Award” from IEEE Trans. on Semiconductor Manufacturing (TSM) 2021
  • Yonghwi Kwon received the “Nick Cobb Memorial Scholarship” from SPIE 2022
  • The “Artificial Intelligence Semiconductor Design SW Development” project, organized by Professor Shin’s lab and jointly studied by seven universities (Seoul National University, Yonsei University, POSTECH, etc.) and two domestic EDA companies (BAUM, Enable Design), was selected in IITP SW computing Industry Technology Development Project Contest.

PUBLICATIONS

  • Seunggyu Lee, Gangmin Cho, Wonjae Lee, Umar Afzaal, and Youngsoo Shin, “Efficient netlist rewriting and in-memory mapping for memristor-aided logic,” Proc. Design Automation Conf. (DAC), submitted.
  • Younggwang Jung, Daijoon Hyun, and Youngsoo Shin, “Accurate interpolation of library timing parameters through stacking regression model,” Proc. Int’l Symp. on Circuits and Systems (ISCAS), submitted.
  • Byungho Choi, Yonghwi Kwon, Umar Afzaal, and Youngsoo Shin, “Multisource clock tree synthesis through sink clustering and fast clock latency prediction,” Proc. Int’l Symp. on Circuits and Systems (ISCAS), submitted.
  • Taeyoung Kim, Gangmin Cho, and Youngsoo Shin, “Block-level power net routing of analog circuit using reinforcement learning,” Proc. Int’l Symp. on Circuits and Systems (ISCAS), submitted.
  • Gangmin Cho, Taeyoung Kim, Byungho Choi, and Youngsoo Shin, “Fast and accurate prediction of process variation band with custom kernels extracted from convolutional networks,” Proc. SPIE Advanced Lithography, accepted.
  • Gangmin Cho, Taeyoung Kim, and Youngsoo Shin, “Fast optical proximity correction using graph convolution network with autoencoders,” IEEE Transactions on Semiconductor Manufacturing, in revision.

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