Anomaly Detection on Multivariate Time-Series from Lithography Equipment using Machine Learning

• Tipo: Tese
• Data: 2023-09-22

Autores

João Gabriel Luís Patrício (Autor) FEUP

Tese

Anomaly Detection on Multivariate Time-Series from Lithography Equipment using Machine Learning

Mestrado

Dissertação

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Outras ciências da engenharia e tecnologias

Instituições Associadas

INESC - INESC TEC - Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência

Outras Informações

Resumo (PT): The present work is mainly motivated by the challenges embraced by the metallic packing industry, in its path along the fourth industrial revolution (Industry 4.0). This work serves to bring Artificial Intelligence (AI) to a mass production lithography process to detect anomalous patterns, using Outlier Detection (OD) algorithms to prevent non-conformities and support quality control operators. All the OD algorithms deployment is based on Machine Learning (ML) techniques, scratching the surface of the process and quality monitoring applications in industrial scenarios.

Abstract (EN): The present work is mainly motivated by the challenges embraced by the metallic packing industry, in its path along the fourth industrial revolution (Industry 4.0). This work serves to bring Artificial Intelligence (AI) to a mass production lithography process to detect anomalous patterns, using Outlier Detection (OD) algorithms to prevent non-conformities and support quality control operators. All the OD algorithms deployment is based on Machine Learning (ML) techniques, scratching the surface of the process and quality monitoring applications in industrial scenarios.

Idioma: Inglês

Nº de páginas: 81