A Deep Learning Model for Robust Wafer Fault Monitoring With Sensor Measurement Noise

By Hoyeop Lee, Young-Joon Kim, Chang Ouk Kim. 2017

98 citations recorded by the source corpus.

Standard fault detection and classification (FDC) models detect wafer faults by extracting features useful for fault detection from time-indexed measurements of the equipment recorded by in situ sensors (sensor signals) and feeding the extracted information into a classifier. However, the preprocessing-and-classification approach often results in the loss of information in the sensor signals that is important for detecting wafer faults. Furthermore, the sensor signals usually contain noise induced by mechanical and electrical disturbances. In this paper, we propose the use of a stacked denoising autoencoder (SdA), which is a deep learning algorithm, to establish an FDC model for simultaneous feature extraction and classification. The SdA model can identify global and invariant features in the sensor signals for fault monitoring and is robust against measurement noise. Through experiments using wafer samples collected from a work-site photolithography tool, we confirmed that as the sensor measurement noise severity increased, the SdA’s classification accuracy could be as much as 14% higher than those of the twelve models considered for comparison, each of which employed one of three feature extractors and one of four classifiers.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering, Industrial and Manufacturing Engineering

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Citations in the corpus

Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, methodology): “For the problem of fault detection and classification (FDC) in noisy settings, [148] employed stacked denoising AEs (SdA) to both reduce the noise of sensory data caused by”
Quo vadis artificial intelligence? (2022, methodology): “Rather than specifying the inputs and the human-defined algorithms to obtain the outputs, an alternative means to solve complex problems is to select the inputs and the expected outputs and to use machines to find the patterns between them automatically and thus “learn” the algorithms [59–61].”
A Deep Learning Approach to Network Intrusion Detection (2018, background): “Deep learning is garnering significant interest and its application is being investigated within many research domains, such as: healthcare [11], [12]; automotive design [13], [14]; manufacturing [15] and law enforcement [16], [17].”