A Convolutional Neural Network for Fault Classification and Diagnosis in Semiconductor Manufacturing Processes

By Ki Bum Lee, S. Cheon, Chang Ouk Kim. IEEE transactions on semiconductor manufacturing, 2016

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Replicating a Trading Strategy by Means of LSTM for Financial Industry Applications (2018, background): “Architectures and models have been investigated in the past two decades, but availability of GPU computing enabled DL industrial applications such as automotive [1], control systems [2], fault detection and recovery [3], [4] and biomedicine [5], [6], just to mention some.”
Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines (2021, background): “CNN-based fault detection and diagnosis models showed their competence in handling multivariate time-series data captured from semiconductor manufacturing processes in [33]–[35].”
Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data (2019, background): “[6] built a convolutional neural network (CNN) to recognize the health conditions of machines.”
Roadmap on multiscale materials modeling (2020, neutral): “Thus, deep learning and artificial intelligence algorithms can be used to solve the inverse problem to determine on-the-fly the actions to minimize defects during processing [122, 123].”