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The semiconductor industry, driven by technological advancements, is continuously undergoing process micronization. This micronization has led to an increased complexity in the wafer fabrication process and equipment. Inevitably, this change leads to a rise in defect rates. Furthermore, the traditional manual analysis methods for these defects possess several limitations. Notably, the defect analysis process is time-consuming and heavily relies on the expertise of the workers, making it challenging to achieve consistent analysis results. In modern semiconductor manufacturing sites, a vast amount of sensor data is being generated in real-time from various equipment. These sensor data contain information on the wafer&#x2019;s condition, the progress of the process, and the operating conditions of the equipment. The purpose of this study is to examine the use of this sensor data in creating a deep learning model that leverages information generated within multi-stage manufacturing processes. The proposed method is designed to automatically classify the defect status of wafers and interpret the causes of these defects. Using the sensor data from two process equipment in semiconductor manufacturing, we achieve better classification performance than other existing methods. Through the interpretation of the classification results, we can identify the sensors and equipment that significantly impact the classification results. The proposed methodology is expected to significantly reduce the time and reliance on engineers for defect analysis, thus greatly improving production efficiency.

Multi-Stage Process Diagnosis Networks in Semiconductor Manufacturing