A Classification-Based Fault Detection and Isolation Scheme for the Ion Implanter

Abstract

We propose a classification-based fault detection and isolation scheme for the ion implanter. The proposed scheme consists of two parts: 1) the classification part and 2) the fault detection and isolation part. In the classification part, we propose a hybrid classification tree (HCT) with learning capability to classify the recipe of a working wafer in the ion implanter, and a k-fold cross-validation error is treated as the accuracy of the classification result. In the fault detection and isolation part, we propose a warning signal generation criteria based on the classification accuracy to detect and fault isolation scheme based on the HCT to isolate the actual fault of an ion implanter. We have compared the proposed classifier with the existing classification software and tested the validity of the proposed fault detection and isolation scheme for real cases to obtain successful results