Best Paper Award

 

Best Paper Award 2021

 

Optical Proximity Correction Using Bidirectional Recurrent Neural Network with Attention Mechanism

Authors: Kwon, Yonghwi; Shin, Youngsoo

 

Honorable Mention Award 2021

Adversarial Defect Detection in Semiconductor Manufacturing Process

Authors: Kim, Do-Nyun; KIM, JAEHOON; Nam, Yunhyoung; Kang, Mincheol; Kim, Kihyun; Hong, Jisuk; Lee, Sooryong

Exploiting 2D Coordinates as Bayesian Priors for Deep Learning Defect Classification of SEM Images

Authors: Susto, Gian Antonio; Arena, Simone; Bodrov, Yury; Carletti, Mattia; Gentner, Natalie; Maggipinto, Marco; Yang, Yao; Beghi, Alessandro; Kyek, Andreas

Wafer Reflectance Prediction for Complex Etching Process Based on K-Means Clustering and Neural Network

Authors: Qiao, Yan; Xiong, Wenqing; Bai, Liping; Ghahramani, Mohammad Hossein; WU, NAIQI; Hsieh, Pinhui; Liu, Bin

 

 

Best Paper Award 2021 Editorial

The IEEE Transactions on Semiconductor Manufacturing congratulates Yonghwi Kwon and Youngsoo Shin whose paper Optical Proximity Correction Using Bidirectional Recurrent Neural Network with Attention Mechanism was selected as the best paper for 2021.  The paper was selected from all the papers that appeared in 2021 by a team of Associate Editors.  This paper applied recurrent neural networks to optical proximity correction for lithographic processing for integrated circuits. The challenge in determining a correction value comes from correlation: correction of one segment affects the correction value of other segments due to the optical proximity effect. This paper broke new ground by showing that Recurrent Neural Networks, which has been mainly applied to time series data, can be effectively applied to spatial data.  Clearly, Machine Learning is rapidly moving from R&D into full flow manufacturing where the interplay between each aspect of a single process step has exponential increased in complexity requiring new approaches.  Three additional papers were recognized with an Honorable Mention:

 

- “Wafer Reflectance Prediction for Complex Etching Process Based on K-Means Clustering

and Neural Network” by Wenqing Xiong, Yan Qiao, Liping Bai, Mohammadhossain

Ghahramani, Naiqi Wu, PinHui Hsieh and Bin Liu (vol. 34 No. 2, May 2021)

- “Adversarial Defect Detection in Semiconductor Manufacturing Process” by Jaehoon Kim,

Yunhyoung Nam, Min-Cheol Kang, Kihyun Kim, Jisuk Hong, Sooryong Lee and Do-Nyun Kim

(vol. 34 No. 3, August 2021)

- “Exploiting 2D Coordinates as Bayesian Priors for Deep Learning Defect Classification of

SEM Images” by Simone Arena, Yury Bodrov, Mattia Carletti, Natalie Gentner, Marco

Maggipinto, Yao Yang, Alessandro Beghi, Andreas Kyek, and Gian Antonio Susto (vol. 34, No.

3, August 2021)

 

The Semiconductor Industry continues to explore new and improved applications of Machine Learning to all aspects of manufacturing.  From writing new process recipes for both fabrication and metrology to process integration to factory control, IEEE Transaction on Semiconductor Manufacturing will continue to publish high quality papers that describe ground-breaking new applications and enhancement of existing approaches for all aspects of manufacturing including machine learning. The entire editorial team for IEEE Transactions on Semiconductor Manufacturing welcomes all authors to submit their papers for review. We also encourage all authors and readers to participate in the manuscript review process.  The 2021 best Paper selection team consisted of Dr. Jeanne Bickford, Dr. Soichi Inoue, and Dr.

Alain Diebold.

 

Dr. Reha Uzsoy

Editor-in-Chief

Transactions on Semiconductor Manufacturing