Title : Aritificial Intelligence for Failure Analysis decisions-flow in Semiconductor Industry 4.0
Beginning of thesis : 05/10/2020
End of thesis : 30/09/2023
Abstract : The Failure Analysis (FA) 4.0 project will address a fundamental challenge for the digital world to ensure that increasingly complex electronic systems operate with complete reliability and safety. Intelligent automation of this analysis decision process using artificial intelligence and machine learning is the objective of the Industry 4.0 consortium; creating a reliable and efficient semiconductor industry.
This research focuses on developing a comprehensive, central recording analysis of metrology parameters and process-related inhomogeneities, tracked along the production process in correlation with existing failure catalogues. Newly discovered failure mechanisms identified in final components will be explored as part of a novel holistic approach for quality assurance and process improvement. The project is characterised by different research challanges linked with available data and the unexhaustive aspect of human decision-making must be considered with adaptive statistical learning tools.
Keywords : Failure analysis; Industry 4.0; Natural language processing; Artificial Intelligence; Machine learning ; Decision theory
Thesis defense : Possibly 06/12/2023
Supervisor :
Mireille Batton-Hubert : Professeur Mines Saint-Étienne, LIMOS
Partners or/and funders : EURIPIDES² PENTA
Project : Penta Euripides project Failure Analysis 4.0
Télécharger la thèse :
Objectifs de développement durable concernés :
Publications
- Failure Analysis (FA) 4.0 is emerging as an essential part of the digital industry, aimed at ensuring maximum reliability and safety of increasingly complex electronic systems in a variety of applications, including autonomous vehicles and digital industrial production (Industry 4.0). At present, failure analysis is mainly carried out by human expertise, involving laborious processes and […]
- Pre-trained large language models (LLMs) have gained significant attention in the field of natural language processing (NLP), especially for the task of text summarization, generation, and question answering. The success of LMs can be attributed to the attention mechanism introduced in Transformer models, which have outperformed traditional recurrent neural network models (e.g., LSTM) in modeling […]
- Failure analysis has become an important part of guaranteeing good quality in the electronic component manufacturing process. The conclusions of a failure analysis can be used to identify a component’s flaws and to better understand the mechanisms and causes of failure, allowing for the implementation of remedial steps to improve the product’s quality and reliability. […]
- Fault analysis (FA) is the process of collecting and analyzing data to determine the cause of a failure. It plays an important role in ensuring the quality in manufacturing process. Traditional FA techniques are time-consuming and labor-intensive, relying heavily on human expertise and the availability of failure inspection equipment. In semiconductor industry, a large amount […]
- Microelectronics production failure analysis is an important step in improving product quality and development. In fact, the understanding of the failure mechanisms and therefore the implementation of corrective actions on the cause of the failure depend on the results of this analysis. These analyses are saved under textual features format. Then such data need first […]
- 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 […]
- 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 […]
- Pre-trained Language Models recently gained traction in the Natural Language Processing (NLP) domain for text summarization, generation and question answering tasks. This stems from the innovation introduced in Transformer models and their overwhelming performance compared with Recurrent Neural Network Models (Long Short Term Memory (LSTM)). In this paper, we leverage the attention mechanism of pre-trained […]
- Variational autoencoders (VAEs) have recently been used for unsupervised disentanglement learning of complex density distributions. Numerous variants exist to encourage disentanglement in latent space while improving reconstruction. However, none have simultaneously managed the trade-off between attaining extremely low reconstruction error and a high disentanglement score. We present a generalized framework to handle this challenge under […]
- 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 […]
- 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 […]
Actualité
- Kenneth Ezukwoke, ancien doctorant au laboratoire LIMOS, qui a brillamment soutenu sa thèse de doctorat intitulée : « Modèles probabilistes à base de graphes intégrés à une architecture de langage de grande taille pour la prise de décision dans l’analyse … Lire la Suite →
- Le Consortium CIROQUO de Recherche et Industrie, publie son rapport d’Activité 2021-2024 Ce rapport met en lumière la contribution significative de l’Institut Fayol de Mines Saint-Étienne au Consortium CIROQUO, avec la participation active de Didier Rullière, Charlie Sire, Tanguy Appriou, … Lire la Suite →
- Le 29 septembre 2023, Kenneth Ezukwoke, doctorant au laboratoire LIMOS, a brillamment soutenu sa thèse de doctorat intitulée : « Modèles probabilistes à base de graphes intégrés à une architecture de langage de grande taille pour la prise de décision … Lire la Suite →
- L’Institut Fayol de Mines Saint-Etienne bien représenté dans les réseaux de l’IFIP (International Federation for Information Processing) Cette année encore, Khaled Medini, chercheur à l’Institut Henri Fayol, IMT, membre du laboratoire LIMOS a poursuivi son implication, commencée il y a … Lire la Suite →