Enhancing Predictive Analytics in Semiconductor Manufacturing: A Deep Learning Approach for Overall Equipment Efficiency Estimation

• Main Authors: Boni, Filippo, De Monte, Riccardo, Yang, Yao, Gentner, Natalie, Low, Joon Khim, Antonio Susto, Gian
• Format: Conference Proceeding
• Language: English
• Subjects: Decision making; Deep learning; Estimation; Feature extraction; Industry 5.0; Manufacturing; Measurement; Overall Equipment Efficiency; Predictive analytics; Predictive models; Semiconductor device manufacture; Semiconductor device modeling; Semiconductor manufacturing; Time series analysis
• ISSN: 2576-3555
• DOI: 10.1109/CoDIT62066.2024.10708503

Efficient decision-making is paramount in manufacturing industries, particularly in sectors like semiconductor manufacturing, which operate within high-demand environments. The semiconductor manufacturing domain, driven by the pervasive utilization of electronics in computing and sensing devices, confronts escalating challenges related to quality control and productivity optimization. This work centers on predicting Overall Equipment Efficiency (OEE), a pivotal metric for pinpointing production efficiency hurdles and refining decision-making processes. Despite its widespread adoption across various industrial domains, there exists a dearth of literature concerning OEE prediction methodologies, with no literature in the context of semiconductor manufacturing. In this work, we propose Deep Learning-based Sequential Learning approaches for OEE estimations. Specifically, we employ the CEEMDAN-GRU model, a deep learning architecture that amalgamates modeling techniques with signal filtering, marking the first instance of its application in OEE prediction. We assess the efficacy of our approach leveraging real-world data sourced from a semiconductor manufacturing facility.