Defect classification on semiconductor wafers using Fisher vector and visual vocabularies coding

Abstract

The main trend in the classification of defects in semiconductor wafers from scanning electron microscopy images is the use of convolutional neural networks. These methods allow automatic feature extraction and supply high accuracy. However, they have limitations since they need a large number of samples in the training phase and are computationally expensive in general. Methods based on visual vocabularies are an efficient alternative to this type of networks that can be implemented with a reduced number of training samples. Although visual vocabularies have been successfully used in different image classification problems, to our knowledge they have never been tested in the classification of defects in semiconductor material wafers from scanning electron microscopy images. On the rectangular region holding the defect, the scale-invariant feature transform algorithm is applied to determine the points of interest and to calculate the descriptors of the patches containing these points. By training a clustering algorithm with these features, a visual vocabulary is created to describe each image as a bag of visual words. Two approaches for coding the descriptors are studied: (i) The more traditional one performs a count of the occurrence of each visual word, and (ii) a modern approach known as Fisher vector which measures the deviation between each descriptor and the generative Gaussian mixture model. Comparing both variants using support vector machines it is seen that Fisher vector coding is computationally more efficient and reports better classification results. The usefulness of visual vocabularies for defect classification on semiconductor wafers is showed by the 91.89% and 93.13% accuracy obtained for bag of visual words and Fisher vector coding, respectively. The training and testing times are lower than 5 min for both approaches.