Skip to Main content Skip to Navigation
Conference papers

β -Variational AutoEncoder and Gaussian Mixture Model for Fault Analysis Decision Flow in Semiconductor Industry 4.0

Abstract : Failure analysis (FA) is key to a reliable semiconductor industry. 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 in semiconductor industry 4.0. As a result, intelligent automation of this analysis decision process using artificial intelligence is the objective of the Industry 4.0 consortium. The research presents natural language processing (NLP) techniques to find a coherent representation of the expert decisions during fault analysis using β-variational autoencoder (β-VAE) for space disentanglement or class discrimination and Gaussian Mixture Model for clustering of the latent space for class identification.
Document type :
Conference papers
Complete list of metadata

https://hal-emse.ccsd.cnrs.fr/emse-03524369
Contributor : Florent Breuil Connect in order to contact the contributor
Submitted on : Thursday, January 13, 2022 - 11:11:42 AM
Last modification on : Saturday, January 15, 2022 - 3:06:37 AM

Identifiers

  • HAL Id : emse-03524369, version 1

Citation

Kenneth Ezukwoke, Anis Hoayek, Mireille Batton-Hubert, Xavier Boucher, Pascal Gounet. β -Variational AutoEncoder and Gaussian Mixture Model for Fault Analysis Decision Flow in Semiconductor Industry 4.0. ENBIS 2021 Spring Meeting, May 2021, Online, France. ⟨emse-03524369⟩

Share

Metrics

Record views

45