Condition assessment and predictive maintenance for contact probe using health index and encoder‐decoder LSTM model

Contact probe is broadly used for the continuous monitoring of microelectronic components in manufacturing industries. False rejection of fine product due to defective contact probe significantly reduces the yield in production. Traditionally, defect detection for contact probes heavily depends on a...

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Bibliographic Details
Published in:Quality and reliability engineering international Vol. 41; no. 1; pp. 154 - 173
Main Authors: Luk, Shun‐Sun, Jin, Yanwen, Zhang, Xiaoge, Ng, Vincent To‐Yee, Huang, Jingyuan, Wong, Chak‐Nam
Format: Journal Article
Language:English
Published: Bognor Regis Wiley Subscription Services, Inc 01.02.2025
Subjects:
Artificial intelligence
Artificial neural networks
Coders
condition assessment
contact probe
Contact resistance
Engineers
Explainable artificial intelligence
False alarms
Feature extraction
health index
Machine learning
Noise prediction
Parameters
Predictive maintenance
sequence‐to‐sequence prediction
ISSN:0748-8017, 1099-1638
Online Access:Get full text
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Summary:Contact probe is broadly used for the continuous monitoring of microelectronic components in manufacturing industries. False rejection of fine product due to defective contact probe significantly reduces the yield in production. Traditionally, defect detection for contact probes heavily depends on a valid range manually defined by engineers over the measured value of certain parameters. However, the subjective range defined according to engineer experience is prone to trigger a high rate of false alarms due to the inherent noise in the measured parameters. To address this issue, we construct a health index (HI) with the contact resistance‐directly‐related features to help monitor and assess the condition of contact probe. Based on the established HI, we develop Long Short‐Term Memory (LSTM) encoder‐decoder machine learning model to assess the condition of contact probe by forecasting the HI value in the future. Encoders from LSTM and convolutional neural network (CNN) are selected as the encoder‐decoder architecture for the sequence‐to‐sequence prediction due to their advantage in extracting the correlation of features at different scales. An explainable Artificial Intelligence (XAI) technique named Local interpretable model‐agnostic explanations (LIME) is used to quantify the contribution of each feature to the model prediction. The encoder from CNN is found to outperform the LSTM encoder in extracting the inter‐feature correlation. Finally, the predicted HI is used to signal the alarm for the maintenance action of contact probe when its value is below a predefined threshold. Comparison between the action alarm triggered by the developed HI and the actual maintenance records suggests that the proposed approach achieves at least 75% accuracy for the triggered alarm in the next 15 mins.
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ISSN:0748-8017
1099-1638
DOI:10.1002/qre.3668