Prediction of Defects Occurrence Time for Capacitive Device

In this paper, we propose an analysis method for defect data of capacitive devices and a method for predicting the occurrence time of defects based on the analysis method. This method is not sensitive to noise in data and has strong robustness. We analyze various features that may affect the occurrence time of capacitive device defects, and use sparse auto encoder (SAE) for dimensionality reduction and denoising. This unsupervised method learns the lowdimensional sparse features of nonlinear manifolds from the data features, which improves the generalization performance of the classifiers to a certain extent. Based on K-nearest regression (KNR), support vector regression (SVR), random forest (RF), gradient boosting decision tree (GBDT) and deep learning (DL) models, we predict the occurrence time of capacitive equipment defects respectively. The root means square error (RMSE) between prediction time and labels is only 243.80 days. Compared with the simple baseline model, the error is reduced by 1773.50 days, and the predicted result is state-of-the-art.