Analysing semiconductor manufacturing big data for root cause detection of excursion for yield enhancement

Abstract

With the shrinking feature size of integrated circuits driven by continuous technology migrations for wafer fabrication, the control of tightening critical dimensions is critical for yield enhancement, while physical failure analysis is increasingly difficult. In particular, the yield ramp up stage for implementing new technology node involves new production processes, unstable machine configurations, big data with multiple co-linearity and high dimensionality that can hardly rely on previous experience for detecting root causes. This research aims to propose a novel data-driven approach for Analysing semiconductor manufacturing big data for low yield (namely, excursions) diagnosis to detect process root causes for yield enhancement. The proposed approach has shown practical viability to efficiently detect possible root causes of excursion to reduce the trouble shooting time and improve the production yield effectively.

Keywords:

• data mining
• big data
• semiconductor manufacturing
• outlier detection
• co-linearity
• excursion

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Ministry of Science and Technology, Taiwan [NSC 100-2628-E-007-017-MY3], [NSC 102-2221-E-007-057-MY3]; Semiconductor Technologies Empowerment Partners Consortium [NSC102-2622-E-007-013], [MOST 103-2218-E-007-023]; Taiwan Semiconductor Manufacturing Company, Taiwan [100A0259JC].