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Conference Papers Year : 2021

Intelligent Fault Analysis Decision Flow in Semiconductor Industry 4.0 Using Natural Language Processing with Deep Clustering

Abstract

Microelectronics production failure analysis is a time-consuming and complicated task involving successive steps of analysis of complex process chains. The analysis is triggered to find the root cause of a failure and its findings, recorded in a reporting system using natural language. Fault analysis, physical analysis, sample preparation and package construction analysis are arguably the most used analysis activity for determining the root-cause of a failure. Intelligent automation of this analysis decision process using artificial intelligence is the objective of the FA 4.0 consortium; creating a reliable and efficient semiconductor industry. This research presents natural language processing (NLP) techniques to find a coherent representation of the expert decisions during fault analysis. The adopted methodology is a Deep learning algorithm based on β-variational autoencoder (β-VAE) for latent space disentanglement and Gaussian Mixture Model for clustering of the latent space for class identification.
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Dates and versions

emse-03325358 , version 1 (24-08-2021)

Identifiers

  • HAL Id : emse-03325358 , version 1

Cite

Kenneth Ezukwoke, Houari Toubakh, Anis Hoayek, Mireille Batton-Hubert, Xavier Boucher, et al.. Intelligent Fault Analysis Decision Flow in Semiconductor Industry 4.0 Using Natural Language Processing with Deep Clustering. IEEE 17th International Conference on Automation Science and Engineering (CASE), Aug 2021, Lyon, France. p 429-436. ⟨emse-03325358⟩
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