Speaker
Kenneth Ezukwoke
(Ecole des Mines de Saint-Etienne)
Description
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.
Primary author
Kenneth Ezukwoke
(Ecole des Mines de Saint-Etienne)
Co-authors
Mr
Anis Hoayek
(Ecole des Mines de Saint-Etienne)
Prof.
Mireille Batton-Hubert
(Mines Saint-Etienne, Univ Clermont Auvergne, CNRS, UMR 6158 LIMOS, Institut Henri Fayol, F - 42023 Saint-Etienne France )
Mr
Xavier Boucher
(Ecole des Mines de Saint-Etienne)
Mr
Pascal Gounet
(STMicroelectronics)