Anomaly detection in semiconductor manufacturing through time series forecasting using neural networks
Name
1083130384-MIT.pdf
Description
Full printable version
Size
9.95 MB
Format
Adobe PDF
Checksum (MD5)
85e335ad8e2afd2e256642f5fea16998
Author(s)
Chen, Tiankai, M. Eng Massachusetts Institute of Technology
Advisor(s)
Duane S. Boning.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
Thesis: M. Eng. in Advanced Manufacturing and Design, Massachusetts Institute of Technology, Department of Mechanical Engineering, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 92-94).
Subjects
Mechanical Engineering.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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