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

被引：0

作者：

Ko, Heung-Kook ^{[1
]}

Park, Sena ^{[1
]}

Ryu, Jihyun ^{[1
]}

Kim, Sung Ryul ^{[1
]}

Lee, Giwon ^{[1
]}

Lee, Dongjoon ^{[1
]}

Pae, Sangwoo ^{[1
]}

Lee, Euncheol ^{[1
]}

Ji, Yongsun ^{[1
]}

Jiang, Hia ^{[1
]}

Jeong, TaeYoung ^{[1
]}

Uemura, Taiki ^{[1
]}

Kwon, Dongkyun ^{[1
]}

Do, Hyungrok ^{[2
]}

Kahng, Hyungu ^{[2
]}

Cho, Yoon Sang ^{[2
]}

Lee, Jiyoon ^{[2
]}

Kim, Seoung Bum ^{[2
]}

机构：

[1] Samsung Elect, Gyeonggi, San 24 Nongseo Dong Giheung Gu, Yongin 446711, Gyeonggi Do, South Korea

[2] Korea Univ, Sch Ind Management Engn, 145 Anam Ro, Seoul 02841, South Korea

来源：

2020 IEEE INTERNATIONAL RELIABILITY PHYSICS SYMPOSIUM (IRPS) | 2020年

关键词：

Gradient Boosting; Machine Learning; Mice; Risk Prediction;

D O I：

10.1109/irps45951.2020.9128932

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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

引用

页数：5

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