Fault Detection of Sensor Data in Semiconductor Processing with Variational Autoencoder Neural Network

Tremendous amounts of equipment parameters and sensor values were generated during modern semiconductor wafer manufacturing. These process data were utilized for early diagnosis of anomalies to prevent subsequent yield loss. However, process data from different steps and machines were highly customized that it is of great difficulties for traditional fault detection and classification (FDC) analysis to find a universal model to identify process excursions. In this paper, we present a neural network method with deep convolutional variational autoencoder structures which used reconstruction error as abnormal scores. This method exhibited a more reliable detection precision than FDC method and traditional anomaly detection algorithms. Furthermore, the reconstruction error of different sensors could be used to identify the root cause of abnormal processes. It was found that similarly structured networks could be applied to different processing steps, which enable this method to be accepted as a standard method to process data.