Defect Classification Using Bayesian Approach for Tape Substrate Inspection System

Abstract

The inspection of ultra-fine pitch patterned tape substrate (TS) requires high resolution optics. In the process of picking out defects at the level of the critical dimension through image processing, however, trivial blemishes formed by dust or micro particles may be detected simultaneously. This leads to unnecessary work on the part of operators reviewing and verifying the additional detected points. To maximize the efficiency of the inspection process, we need to identify and classify the defect candidates whether it is a real pattern defect or simply a trivial blemish by dust. In this article, we propose a Bayesian approach to classify the defective images based on the measures of the image features. The features of the defective region in terms of shape and brightness are obtained from a series of proper image analysis with FFT. Based on the data collected from experiments, we devised a statistic model for classification.