AI-based Modeling and Data-driven Evaluation for Smart Manufacturing Processes

TL;DR

This paper explores AI and data analytics techniques, including evolutionary computing and deep learning, to optimize semiconductor manufacturing processes and enhance predictive capabilities in smart manufacturing.

Contribution

It introduces a dynamic algorithm combining genetic algorithms and neural networks for feature selection and process control in semiconductor manufacturing.

Findings

Effective feature selection algorithm developed

Improved process control through AI techniques

Enhanced predictive insights for manufacturing

Abstract

Smart Manufacturing refers to optimization techniques that are implemented in production operations by utilizing advanced analytics approaches. With the widespread increase in deploying Industrial Internet of Things (IIoT) sensors in manufacturing processes, there is a progressive need for optimal and effective approaches to data management. Embracing Machine Learning and Artificial Intelligence to take advantage of manufacturing data can lead to efficient and intelligent automation. In this paper, we conduct a comprehensive analysis based on Evolutionary Computing and Deep Learning algorithms toward making semiconductor manufacturing smart. We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes and to address various challenges. We elaborate on the utilization of a Genetic Algorithm and Neural Network to propose an intelligent feature selection algorithm. Our objective is to provide an advanced solution for controlling manufacturing processes and to gain perspective on various dimensions that enable manufacturers to access effective predictive technologies.