Data mining and fault diagnosis based on wafer acceptance test data and in-line manufacturing data

被引：8

作者：

Fan, CM ^{[1
]}

Guo, RS ^{[1
]}

Chen, A ^{[1
]}

Hsu, KC ^{[1
]}

Wei, CS ^{[1
]}

机构：

[1] Asiatek Inc, R&D Div, Taipei, Taiwan

来源：

2001 IEEE INTERNATIONAL SYMPOSIUM ON SEMICONDUCTOR MANUFACTURING, CONFERENCE PROCEEDINGS | 2001年

关键词：

D O I：

10.1109/ISSM.2001.962941

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This paper focuses on techniques for automatically extracting process knowledge from production database for fault diagnosis and optimizing device performance with fixed target. An integrated parametric analysis scheme is developed with supplemented graphical methods to facilitate interpretation of results. It consists of five phases: Device Variation Partition, Key Node Screening, Linear Equipment Modeling, Graph Aided Interpretation, and Control Policy Re-evaluation. The concepts of quality control, data mining, and process knowledge are integrated in this scheme. Field data case study shows that the integrated parametric analysis scheme is able to diagnose the parametric yield problem, help engineers construct the knowledge base, predict the yield, and provide insights for yield enhancement.

引用

页码：171 / 174

页数：4