Defect Detection on Semiconductor Wafers by Distribution Analysis

2021/11/05 by Olschewski, Thomas
#68T09 #68T20 #FOS: Computer and information sciences #I.2.1 #I.2.6 #Machine Learning (cs.LG)

paper · doi:10.48550/arxiv.2111.03727

Abstract

A method for object classification that is based on distribution analysis is proposed. In addition, a method for finding relevant features and the unification of this algorithm with another classification algorithm is proposed. The presented classification algorithm has been applied successfully to real-world measurement data from wafer fabrication of close to hundred thousand chips of several product types. The presented algorithm prefers finding the best rater in a low-dimensional search space over finding a good rater in a high-dimensional search space. Our approach is interesting in that it is fast (quasi-linear) and reached good to excellent prediction or detection quality for real-world wafer data.

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