Dynamic Time Warping Constraints for Semiconductor Processing

Semiconductor manufacturing processes have become increasingly complex with the continued growth of chip manufacturing. Monitoring these processes for anomalies is crucial for maintaining quality and yield. However, a notable challenge for monitoring time series signals are the nonlinear variations...

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Bibliographic Details
Main Author: Owens, Rachel
Other Authors: Boning, Duane S.
Format: Thesis
Published: Massachusetts Institute of Technology 2024
Online Access:https://hdl.handle.net/1721.1/156276
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author Owens, Rachel
author2 Boning, Duane S.
author_facet Boning, Duane S.
Owens, Rachel
author_sort Owens, Rachel
collection MIT
description Semiconductor manufacturing processes have become increasingly complex with the continued growth of chip manufacturing. Monitoring these processes for anomalies is crucial for maintaining quality and yield. However, a notable challenge for monitoring time series signals are the nonlinear variations in signal timing. These small, but acceptable, temporal variations are typically caused by small run-to-run differences that are inherent to the process. Dynamic time warping (DTW) can be used for temporal alignment of signals, but is computationally expensive and prone to errors. In this thesis, a new method is presented for preprocessing semiconductor fabrication sensor signals that improves anomaly detection model performance. The new method uses domain knowledge – specifically, process recipe step numbers – to create constraints that better align signals along the time dimension, that addresses this problem of nonlinear signal alignment. These constraints are tested on both synthetic as well as industrial datasets. The new step-constrained DTW is also extended as a distance measure for clustering time series.
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spelling mit-1721.1/1562762024-08-22T03:42:31Z Dynamic Time Warping Constraints for Semiconductor Processing Owens, Rachel Boning, Duane S. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Semiconductor manufacturing processes have become increasingly complex with the continued growth of chip manufacturing. Monitoring these processes for anomalies is crucial for maintaining quality and yield. However, a notable challenge for monitoring time series signals are the nonlinear variations in signal timing. These small, but acceptable, temporal variations are typically caused by small run-to-run differences that are inherent to the process. Dynamic time warping (DTW) can be used for temporal alignment of signals, but is computationally expensive and prone to errors. In this thesis, a new method is presented for preprocessing semiconductor fabrication sensor signals that improves anomaly detection model performance. The new method uses domain knowledge – specifically, process recipe step numbers – to create constraints that better align signals along the time dimension, that addresses this problem of nonlinear signal alignment. These constraints are tested on both synthetic as well as industrial datasets. The new step-constrained DTW is also extended as a distance measure for clustering time series. S.M. 2024-08-21T18:53:17Z 2024-08-21T18:53:17Z 2024-05 2024-07-10T12:59:48.388Z Thesis https://hdl.handle.net/1721.1/156276 In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology
spellingShingle Owens, Rachel
Dynamic Time Warping Constraints for Semiconductor Processing
title Dynamic Time Warping Constraints for Semiconductor Processing
title_full Dynamic Time Warping Constraints for Semiconductor Processing
title_fullStr Dynamic Time Warping Constraints for Semiconductor Processing
title_full_unstemmed Dynamic Time Warping Constraints for Semiconductor Processing
title_short Dynamic Time Warping Constraints for Semiconductor Processing
title_sort dynamic time warping constraints for semiconductor processing
url https://hdl.handle.net/1721.1/156276
work_keys_str_mv AT owensrachel dynamictimewarpingconstraintsforsemiconductorprocessing