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Duane S. Boning. Chen, Tiankai, M. Eng Massachusetts Institute of Technology Massachusetts Institute of Technology. Department of Mechanical Engineering. Massachusetts Institute of Technology. Department of Mechanical Engineering 2019-02-05T16:00:45Z 2019-02-05T16:00:45Z 2018 2018 http://hdl.handle.net/1721.1/120245 1083130384 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). 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. by Tiankai Chen. M.Eng. in Advanced Manufacturing and Design 101 pages eng Massachusetts Institute of Technology 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. http://dspace.mit.edu/handle/1721.1/7582 Mechanical Engineering. Anomaly detection in semiconductor manufacturing through time series forecasting using neural networks Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="3e62c23b-d984-4f2d-ba99-5c7154513550"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Anomaly detection in semiconductor manufacturing through time series forecasting using neural networks</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2018</PublicationDate> <Authors> <Author> <DisplayName>Chen, Tiankai, M. Eng Massachusetts Institute of Technology</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Mechanical Engineering.</Keyword> <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.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>