ANALYZING TFT-LCD ARRAY BIG DATA FOR YIELD ENHANCEMENT AND AN EMPIRICAL STUDY OF TFT-LCD MANUFACTURING IN TAIWAN

Authors

  • Pei-Chun Chu National Tsing Hua University
  • Chia-Cheng Chen National Tsing Hua University
  • Chen-Fu Chien National Tsing Hua University

DOI:

https://doi.org/10.23055/ijietap.2016.23.5.3063

Keywords:

Data mining, Decision tree, Kruskal–Wallis test, Yield enhancement, TFT-LCD, Mura

Abstract

The flat panel display industry has invested considerable resources in constructing large-size panels and rendered the process more complex, resulting in various defects and low yield. Engineers rely on their domain knowledge or rules of thumb for troubleshooting; however, limited domain knowledge, insufficient experience, faulty generalization, and bounded rationality lead to ineffective judgment. The objective of this study was to develop a framework for data mining and knowledge discovery from a database; the Kruskal–Wallis test and a decision tree were used to investigate a large amount of thin film transistor-liquid crystal display (TFT-LCD) manufacturing data and determine the possible causes of faults and manufacturing process variations. An empirical study was conducted at a TFT-LCD company in Taiwan, and the results demonstrated the practical viability of the framework.

Author Biographies

Pei-Chun Chu, National Tsing Hua University

Ph.D. Candidate

Department of Industrial Engineering & Engineering Management

Chia-Cheng Chen, National Tsing Hua University

Post-doc Researcher,

Department of Industrial Engineering & Engineering Management 

Chen-Fu Chien, National Tsing Hua University

Tsinghua Chair Professor

Department of Industrial Engineering & Engineering Management,

Published

2017-01-06

How to Cite

Chu, P.-C., Chen, C.-C., & Chien, C.-F. (2017). ANALYZING TFT-LCD ARRAY BIG DATA FOR YIELD ENHANCEMENT AND AN EMPIRICAL STUDY OF TFT-LCD MANUFACTURING IN TAIWAN. International Journal of Industrial Engineering: Theory, Applications and Practice, 23(5). https://doi.org/10.23055/ijietap.2016.23.5.3063

Issue

Section

Special Issue: 2015 International Symposium on Semiconductor Manufacturing Intelligence