Professor Kim Do-nyeon and his research team from the Department of Mechanical Engineering at Seoul National University have received the 2024 Best Paper Award from the international academic journal 'IEEE Transactions on Semiconductor Manufacturing (IEEE TSM)'.
IEEE TSM covers the latest technologies and applications related to semiconductor processes and production. One paper among those published in the journal over the year is selected and awarded as the best paper. Professor Kim Do-nyeon's team was previously recognized as one of the three excellent papers in 2021, and they have now received the best paper award from the same journal after three years.
The research team presented a deep learning technology capable of predicting vulnerable areas where defects may occur during the lithography process using only lithography pattern information. This technology has been evaluated as a key method to enhance semiconductor production yield and reduce expenses through proactive design changes for vulnerable areas.
The lead author, Dr. Kim Jae-hoon, said, "It is a great honor to receive such a meaningful award, and I would like to thank all those who participated in the research." He added, "I will use this achievement as a stepping stone to dedicate myself to research on measurement and inspection technology in semiconductor processes." Dr. Kim Jae-hoon is currently continuing his research activities as a postdoctoral researcher in the Department of Mechanical Engineering at Seoul National University.
Co-author Dr. Lim Jae-kyung said, "I am very happy that the results of this study were selected as the best paper of 2024, and I want to express my gratitude to many who supported the research," and added, "I will continue to strive for advancements in the semiconductor manufacturing field." Dr. Lim Jae-kyung, who obtained his doctoral degree from the Department of Mechanical Engineering at Seoul National University, is currently working at Samsung Electronics, using scanning microscopy and electron beam inspection to detect semiconductor defects.
References
IEEE TSM (2023), DOI: https://ieeexplore.ieee.org/document/10297297