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.

Li-ion batteries have become the most important technology for electric mobility. One of the most pressing chal-lenges is the development of reliable methods for battery state-of-health (SOH) diagnosis and estimation of remaining useful life. In electric mobility scenario, battery capacity degradation prediction is crucial to ensure service availability and life duration. This research work provides a comprehensive comparative analysis of neural networks for a data-driven approach suitable for SOH estimation on single cells, stressed under laboratory conditions. For this purpose, different neural networks (i.e., LSTM, GRU, 1D-CNN, CNN-LSTM) are trained and optimized on NASA Randomized Battery Usage dataset. Experimental results demonstrate that data-driven neural networks generally performed well SOH estimation on single cells. In detail, the 1D-CNN best predicts SOH and has the lowest variance in the output. The LSTM have the highest variance in estimating SOH, while GRU and CNN-LSTM tend to overestimate and underestimate the value of SOH, respectively.