Anomaly detection in semiconductor manufacturing through time series forecasting using neural networks

Semiconductor manufacturing provides unique challenges to the anomaly detection problem. With multiple recipes and multivariate data, it is difficult for engineers to reliably detect anomalies in the manufacturing process. An experimental study into anomaly detection through time series forecasting is carried out with application to a plasma etch case study. The study is performed on three predictive models with increasing complexity for comparison. The three models are namely: Autoregressive Integrated Moving Average (ARIMA), Multi-Layer Perceptron (MLP) and Long Short Term Memory (LSTM). ARIMA is a statistical model while MLP and LSTM are neural network models. The results from the control experiment, under supervised training, shows the validity of MLP and LSTM in detecting anomalies through time series forecasting with a recall accuracy of 92% for the best model. Conversely, the ARIMA model has a relatively poor performance due to the inability to model the data correctly. Experimental results also display the ability of neural network models to adapt to training sets of multiple recipes. Furthermore, downsampling is explored to reduce training times and has been found to have minor effects on the accuracy of the model. Moreover, an unsupervised approach towards anomaly detection is found to have little success in detecting anomalous points in the data.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Chen, Tiankai, M. Eng Massachusetts Institute of Technology

Advisor dc:contributor.advisor

• Duane S. Boning.

• Mechanical Engineering.

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

http://hdl.handle.net/1721.1/120245

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/120245