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International audience; Microelectronics production failure analysis is an important step in improving product quality and development. Indeed, the understanding of the failure mechanisms and therefore the implementation of corrective actions on the cause of the failure depend on the results of these analysis. These analysis are saved under textual features format. Then such data need first to be pre-processed and vectorized (converted to numeric). Second, to overcome the curse of dimensionality caused by the vectorisation process, a dimension reduction is applied. We are first interested in studying the potential of using an unsupervised variable selection technique to identify the variables that best demonstrate discrimination in the separation and compactness of groups of textual data. Variable selection has been approached by several variable or feature selection methods. Some of them have not been adapted for use in large data sets or are difficult to tune, and others require additional information. This work investigates the potential of using a genetic algorithm to find, in an unsupervised way, the variables allowing the best discrimination of the classes, to select variables correlated to particular textual groups. The proosed genetic algorithm uses a combination of the K-means clustering and validity indices as a fitness function for optimization. Such a function improves both compactness and class separation. Experiments on textual datasets demonstrate the effectiveness of the proposed method of variable selection which allows better discrimination of textual classes compared to the use of K-means clustering on all data variables.

International audience; 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.