Fault classification and timing prediction based on shipment inspection data and maintenance reports for semiconductor manufacturing equipment

Abstract

Unexpected failures of equipment in sequential and continuous manufacturing processes such as semiconductor production can result in a serious deterioration of productivity. This directly affects the interests of equipment operators and indirectly reduces the credibility of the equipment manufacturers, resulting in serious potential harm to operators and manufacturers of equipment. Hence, the demand for predictive maintenance has increased to maximize the efficiency of equipment operations by predicting faults and fault timing beyond preemptive preventive maintenance, which depends on periodic inspections while operating equipment. However, since existing studies on predictive maintenance mainly utilize data collected during the equipment operation stage to predict equipment faults and fault timing, predictive maintenance research has only been conducted from the perspective of equipment operating companies. Accordingly, we propose a machine learning method in this paper that predicts the existence of faults and the fault timing of equipment using pre-shipment inspection data from equipment manufacturers. Importantly, this can be performed by an equipment manufacturer without access to operational data. This is the first predictive maintenance study to be conducted from the perspective of an equipment manufacturing company. We used tree-based ensemble models to identify important variables for faults and proceeded to predict the existence of faults and the first fault timing of equipment. Moreover, we verified the method’s performance using real data from a leading semiconductor equipment manufacturer in South Korea. The contribution of this study can be summarized as follows. (1) We propose an initial predictive maintenance method that can be performed by equipment manufacturing companies. (2) As a result of an experiment using real data from the selected manufacturer, the proposed method yielded an accuracy of up to 94% for fault existence prediction and reduced the prediction error by more than 50% for the first fault timing compared to where a prediction model was not implemented. (3) It was demonstrated that the initial state of a piece of equipment has a great influence on future faults by deriving high performance using pre-shipment inspection data rather than operation data. (4) By using tree-based ensemble models, we identified major factors related to equipment faults.