Early Diagnosis and Prediction of Wafer Quality Using Machine Learning on sub-10nm Logic Technology

Heung Kook Ko, Sena Park, Jihyun Ryu, Sung Ryul Kim, Giwon Lee, Dongjoon Lee, Sangwoo Pae, Euncheol Lee, Yongsun Ji, Hia Jiang, Tae Young Jeong, Taiki Uemura, Dongkyun Kwon, Hyungrok Do, Hyungu Kahng, Yoon Sang Cho, Jiyoon Lee, Seoung Bum Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes to use machine learning (ML) methods to predict wafer quality using Fab inline measured items, DC measurements, and DVS (Dynamic Voltage Stress) at wafer sort. With developed ML approach, the predicted accuracy is more than 80% in 8 nm products used in this study. We believe this method can be further fine-tuned to help enable ICs at the high level expected for automotive systems. By assigning predictive rankings, the method also helps enable best tooling system for higher quality.

Original languageEnglish
Title of host publication2020 IEEE International Reliability Physics Symposium, IRPS 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728131993
DOIs
Publication statusPublished - 2020 Apr
Event2020 IEEE International Reliability Physics Symposium, IRPS 2020 - Virtual, Online, United States
Duration: 2020 Apr 282020 May 30

Publication series

NameIEEE International Reliability Physics Symposium Proceedings
Volume2020-April
ISSN (Print)1541-7026

Conference

Conference2020 IEEE International Reliability Physics Symposium, IRPS 2020
CountryUnited States
CityVirtual, Online
Period20/4/2820/5/30

Keywords

  • Gradient Boosting
  • Machine Learning
  • Mice
  • Risk Prediction

ASJC Scopus subject areas

  • Engineering(all)

Fingerprint Dive into the research topics of 'Early Diagnosis and Prediction of Wafer Quality Using Machine Learning on sub-10nm Logic Technology'. Together they form a unique fingerprint.

  • Cite this

    Ko, H. K., Park, S., Ryu, J., Kim, S. R., Lee, G., Lee, D., Pae, S., Lee, E., Ji, Y., Jiang, H., Jeong, T. Y., Uemura, T., Kwon, D., Do, H., Kahng, H., Cho, Y. S., Lee, J., & Kim, S. B. (2020). Early Diagnosis and Prediction of Wafer Quality Using Machine Learning on sub-10nm Logic Technology. In 2020 IEEE International Reliability Physics Symposium, IRPS 2020 - Proceedings [9128932] (IEEE International Reliability Physics Symposium Proceedings; Vol. 2020-April). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IRPS45951.2020.9128932