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.
@article{arxiv.2111.03727,
title = {Defect Detection on Semiconductor Wafers by Distribution Analysis},
author = {Thomas Olschewski},
journal= {arXiv preprint arXiv:2111.03727},
year = {2021}
}