Dr. Soo-Yong Lee
Principal Engineer at SAMSUNG Electronics Co Ltd
SPIE Involvement:
Author
Area of Expertise:
OPC , PPC , RET , Machine Learning , AI , DFM
Profile Summary

Dr. Sooyong Lee is a principal engineer at Samsung Semiconductor Research and Development (SRD). He holds a bachelor's and doctorate in physics from POSTECH, specializing in theoretical condensed matter physics. His experience includes postdoctoral research at POSTECH and Sungkyunkwan University, as well as a visiting researcher role at MIT. Initially, his research focused on electronic interferometers and topological matter in mesoscopic and many-body systems. After joining SRD, Dr. Lee transitioned his career towards Optical Proximity Correction (OPC). He has since been instrumental in developing resolution enhancement technologies for fine feature patterning in memory devices. Furthermore, Dr. Lee has significantly improved accuracy and reduced dispersion on wafers by implementing OPC and Process Proximity Correction (PPC) with machine learning. Recently, he completed an AI expert training program at Seoul National University and is currently spending a research year at Stanford University as a visiting researcher. His current interest lies in integrating cutting-edge AI technologies into OPC and PPC processes.
Publications (4)

SPIE Journal Paper | 26 May 2024
JM3, Vol. 23, Issue 02, 021303, (May 2024) https://doi.org/10.1117/12.10.1117/1.JMM.23.2.021303
KEYWORDS: Transformers, Visualization, Inspection, Design, Performance modeling, Image classification, Defect detection, Optical proximity correction, Education and training, Deep learning

Proceedings Article | 21 November 2023 Presentation + Paper
Proceedings Volume 12751, 1275109 (2023) https://doi.org/10.1117/12.2685488
KEYWORDS: Transformers, Visualization, Design and modelling, Deep learning, Image classification, Data modeling

Proceedings Article | 1 May 2023 Presentation + Paper
Proceedings Volume 12499, 124990C (2023) https://doi.org/10.1117/12.2657880
KEYWORDS: Etching, Data modeling, Modeling, Machine learning, Design and modelling, Statistical modeling, Optical proximity correction, Reflection, Feature extraction, Artificial intelligence

Proceedings Article | 28 April 2023 Paper
Proceedings Volume 12495, 124951S (2023) https://doi.org/10.1117/12.2658321
KEYWORDS: Optical proximity correction, Machine learning, Artificial neural networks

SIGN IN TO:
  • View contact details

UPDATE YOUR PROFILE
Is this your profile? Update it now.
Don’t have a profile and want one?

Advertisement
Advertisement
Back to Top