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 ap...

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on semiconductor manufacturing Vol. 35; no. 2; p. 154
Main Author Diebold, Alain
Format Journal Article
LanguageEnglish
Published New York The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 01.01.2022
Subjects
Integrated circuits
Machine learning
Manufacturing
Neural networks
Optical communication
Proximity
Proximity effect (electricity)
Recurrent neural networks
Segments
Spatial data
Online AccessGet full text

Cover

More Information
Summary: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:
ISSN:0894-6507
1558-2345
DOI:10.1109/TSM.2022.3162347