교원소개

본 연구실은 Moore’s law 한계를 극복하기 위해  More Moore 및 More than Moore와 같은 방식의  차세대 반도체 개발 및 최적화를 물리적 모델링과 머신 러닝을 활용하여 진행하고 있습니다.  다양한 종류의 신소자, 신개념의  반도체  최적화를 위해 재료, 공정, 소자, 회로 로 이어지는 Mutli-domain, Multi-scale 모델링 플랫폼 개발을 위해 물리 모델과 머신 러닝의 장점을 융합하기 위해 역량을 집중하고 있습니다.

– Curriculum Vitae

• 2022-Present: Associate Professor, UNIST
• 2003-2007, 2012-2022: Principal Engineer, Samsung Electronics

– Academic Credential

• 2012: Ph.D., Electrical and Computer Engineering, Purdue University
• 2001: M.S.,  Materials Science and Engineering, Seoul National University
• 1999: B.S., Materials Science and Engineering, Seoul National University

– Research  Keywords and Topics

Physics and technology of nanoelectronic materials and devices, Physical simulation and machine learning based design

– Publications (selected) 

• “Bridging TCAD and AI: Its Application to Semiconductor Design.”, C. Jeong et al., IEEE Transactions on Electron Devices 2021, 68(11), 5364–71
• “Restructuring TCAD System: Teaching Traditional TCAD New Tricks.”, S. Myung, et al., IEEE International Electron Devices Meeting,  2021, 18.2.1-18.2.4,
• “Prediction and Interpretation of Polymer Properties Using the Graph Convolutional Network”, J. Park et al., ACS polymers,  2022, 2(4), 213-222

“PAC-Net: A Model Pruning Approach to Inductive Transfer Learning”, S. Myung, et al., International Conference on Machine Learning,  2022, 16240-16252