Early Detection and Classification of Bearing Faults in Semiconductor Materials towards Sustainable Manufacturing Process

Machine learning (ML) approaches have gained significant attention in recent years for their ability to identify patterns and anomalies in large and complex datasets. In the context of semiconductor manufacturing, ML techniques are being used for fault detection and classification to improve the overall quality of the manufacturing process. Fault detection is critical in ensuring that semiconductor materials meet the required specifications and standards, which is essential for sustainable manufacturing processes. ML approaches for fault detection in semiconductor materials involve the use of algorithms to analyze data from various sensors and measurements, including temperature, pressure, and vibration sensors. The algorithms can identify patterns in the data and detect any anomalies that may indicate a fault or defect in the manufacturing process. ML algorithms can also be used to classify faults and identify their root causes, which can help improve the manufacturing process and reduce waste. One of the most technologically challenging industrial processes is the production of semiconductors. Long-established machine learning techniques like univariate and multivariate analysis have been used to create predictive models that can identify failures. Predictive modelling has been the subject of large collaborative research programme over the past ten years between universities and fantastic businesses. In this article, we take a closer look at some of these study topics before recommending machine learning techniques to automatically produce an accurate predictive model to forecast equipment failures during the semiconductor industry’s wafer production process. The goal of this research project is to create a decision model that will aid in immediately identifying any equipment breakdown in order to maintain high process yields in production.