Fault diagnosis for ion mill etching machine cooling system based on omni-dimensional dynamic convolution and dynamic spatial pyramid pooling

Ion mill etching machine is a critical equipment in semiconductor manufacturing for wafer etching processes. The cooling system, as an integral part of the Ion mill etching machine, is prone to malfunctions during operation, which can result in the scrapping of subsequent wafers processed by the equipment. Therefore, early detection of cooling system faults is crucial to prevent wafer wastage. Due to the high flexibility of the processing process in ion beam etching machines, the length of time-series signals collected by sensors varies significantly, which poses a challenge for accurate fault diagnosis of the cooling system in ion mill etching machines. To address these issues, we propose a novel end-to-end hybrid model called ODCNN-DSPP. The main contributions are as follows: (1) Enhancing the convolutional neural network (CNN) model structure by introducing an omni-dimensional dynamic convolutional layer to improve feature extraction capabilities; and (2) Utilising a dynamic spatial pyramid pooling method to mitigate the impact of irrelevant features on the CNN. Using publicly available ion mill etching machine datasets for experiments, the results indicate that the method proposed in this paper outperforms current mainstream fault diagnosis methods, achieving an average fault diagnosis accuracy of 96.55% on the test set.