Anomaly Detection in Batch Manufacturing Processes Using Localized Reconstruction Errors From 1-D Convolutional AutoEncoders

Mark Gorman, Xuemei Ding, Liam Maguire, Damien Coyle

Research output: Contribution to journalArticlepeer-review

3 Citations (SciVal)
63 Downloads (Pure)

Abstract

Multivariate batch time-series data sets within Semiconductor manufacturing processes present a difficult environment for effective Anomaly Detection (AD). The challenge is amplified by the limited availability of ground truth labelled data. In scenarios where AD is possible, black box modelling approaches constrain model interpretability. These challenges obstruct the widespread adoption of Deep Learning solutions. The objective of the study is to demonstrate an AD approach which employs 1-Dimensional Convolutional AutoEncoders (1d-CAE) and Localised Reconstruction Error (LRE) to improve AD performance and interpretability. Using LRE to identify sensors and data that result in the anomaly, the explainability of the Deep Learning solution is enhanced. The Tennessee Eastman Process (TEP) and LAM 9600 Metal Etcher datasets have been utilised to validate the proposed framework. The results show that the proposed LRE approach outperforms global reconstruction errors for similar model architectures achieving an AUC of 1.00. The proposed unsupervised learning approach with AE and LRE improves model explainability which is expected to be beneficial for deployment in semiconductor manufacturing where interpretable and trustworthy results are critical for process engineering teams.
Original languageEnglish
Pages (from-to)147-150
Number of pages4
JournalIEEE Transactions on Semiconductor Manufacturing
Volume36
Issue number1
DOIs
Publication statusPublished - 1 Feb 2023

Bibliographical note

Tier 2 High Performance Computing Resources through the Northern Ireland High Performance Computing (NI-HPC) Facility, funded by the U.K. Engineering and Physical Sciences Research Council (EPSRC) (Grant Number: EP/T022175/1)
10.13039/100012338-UKRI Turing AI Fellowship 2021-2025, funded by The Alan Turing Institute and the EPSRC (Grant Number: EP/V025724/1)
SmartNanoNI Project funded by the UKRI Strength and Places Fund

Keywords

  • Deep learning
  • convolutional autoencoder
  • fault detection and classification
  • reconstruction error
  • semiconductor manufacturing

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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