In the semiconductor industry, Failure Analysis (FA) is an investigation to determine the root causes of a failure. It also involves an intermediate analysis to build the steps of the failure analysis in order to mitigate future failures and to facilitate the future FA. In the framework of the FA 4.0 project, the reporting system records three items of information using natural language: the failure analysis request description (input space) and analysis steps (paths), as well as generic categories of root cause conclusion (output space). The main objective of this article is to develop a system which is able to automatically help industries carry out fault analysis diagnoses with Artificial intelligence (AI). This article extends and validates the adapted methodology proposed by [1] to transform text data into numeric data based on Natural Language Processing (NLP). It transforms the text data from the input space and output space. Different deep learning algorithms based on a Variational AutoEncoder (VAE) are applied to the output space to reduce the dimension of the numeric data, and the performance of each VAE is evaluated with different metrics. The Generalized-Controllable VAE (GCVAE) is the one best suited to our case. A Gaussian Mixture Model (GMM) is then used to perform clustering in the latent space generated by the GCVAE. A centroid analysis is also conducted to verify the similarity of each cluster.
