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 runtimes.
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 runtimes.
| Original language | English |
|---|---|
| Pages | 7-13 |
| Number of pages | 6 |
| Publication status | Published (in print/issue) - 25 Oct 2020 |
Keywords
- Defect Detection
- Defect Classification, ,
- Image Processing
- Bag of Visual Words
- Semiconductor wafers
- Local Features
