Home // EMERGING 2020, The Twelfth International Conference on Emerging Networks and Systems Intelligence // View article

Semiconductor Defect Classifcation

Authors:
Terence Sweeney
Sonya Coleman
Dermot Kerr

Keywords: Defect Detection; Defect Classification; Bag of Visual Words; Local Features; Semiconductor wafers; Image Processing.

Abstract:
Automated inspection has become a vital part of quality control during semiconductor wafer production. Current processes are focussed on finding defects via variation from a ‘golden’ image using pixel to pixel comparisons or utilization of opaque neural network-based approaches. We present a novel approach, which uses the Bag of Visual Words technique to determine local features that correspond to specific defects within a wafer image, known as a custom vocabulary, as a way to begin creation of a more transparent system for automated defect detection and classification. We demonstrate that the custom vocabularies, combined with machine learning algorithms, result in high performance accuracies with efficient computational run-times.

Pages: 7 to 12

Copyright: Copyright (c) IARIA, 2020

Publication date: October 25, 2020

Published in: conference

ISSN: 2326-9383

ISBN: 978-1-61208-815-0

Location: Nice, France

Dates: from October 25, 2020 to October 29, 2020