Machine learning for automated anomaly detection in semiconductor manufacturing

In the realm of semiconductor manufacturing, detecting anomalies during manufacturing processes is crucial. However, current methods of anomaly detection often rely on simple excursion detection methods, and manual inspection of machine sensor data to determine the cause of a problem. In order to improve semiconductor production line quality, machine learning tools can be developed for more thorough and accurate anomaly detection. Previous work on applying machine learning to anomaly detection focused on building reference cycles, and using clustering and time series forecasting to detect anomalous wafer cycles. We seek to improve upon these techniques and apply them to related domains of semiconductor manufacturing. The main focus is to develop a process for automated anomaly detection by combining the previously used methods of cluster analysis and time series forecasting and prediction. We also explore detecting anomalies across multiple semiconductor manufacturing machines and recipes.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• DeLaus, Michael Daniel.

Advisor dc:contributor.advisor

• Duane S. Boning.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/123017

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/123017