Particle Swarm Optimization Based Deep Learning Ensemble for Manufacturing Processes

Abstract

Faults detection in semiconductor manufacturing processes is a challenging research problem because the manufacturing processes are characterized by hundreds of different types of operations and the identification of those operations that lead to faults in the final products is very challenging. The existence of faults during the manufacturing processes is strongly correlated with the performance of the manufacturing processes and in this article the main challenge that is approached is the accurate classification of the cases in which the manufacturing processes pass or fail the in line testing considering the information collected during the execution of the operations that are involved in the manufacturing processes. This article approaches the classification of the in line testing of the manufacturing processes using a method that is based on an ensemble of deep learning models in which the weights of the models are determined using Particle Swarm Optimization (PSO). The method is tested and validated on the open source SECOM dataset from the UCI Machine Learning Repository.