Abstract
Automated inspection has become a vital part of quality control in many industries, including during
semiconductor wafer production. Current processes often focus on finding defects by comparing images
with a ‘golden’ image pixel to pixel or, more recently, using shallow or deep learning based approaches.
We present an alternative 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. Using
this custom vocabulary combined with machine learning, we can characterise and accurately classify
defects found on wafer images.
semiconductor wafer production. Current processes often focus on finding defects by comparing images
with a ‘golden’ image pixel to pixel or, more recently, using shallow or deep learning based approaches.
We present an alternative 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. Using
this custom vocabulary combined with machine learning, we can characterise and accurately classify
defects found on wafer images.
| Original language | English |
|---|---|
| Pages | 93-96 |
| Number of pages | 4 |
| Publication status | Published (in print/issue) - 30 Aug 2020 |
| Event | Irish Machine Vision and Image Processing Conference (IMVIP) 2020 - ITSligo / Virtual , Ireland Duration: 31 Aug 2020 → 2 Sept 2020 https://imvipconference.github.io/#proceedings |
Conference
| Conference | Irish Machine Vision and Image Processing Conference (IMVIP) 2020 |
|---|---|
| Abbreviated title | IMVIP 2020 |
| Country/Territory | Ireland |
| Period | 31/08/20 → 2/09/20 |
| Internet address |
Keywords
- Defect Detection
- Local Features
- Classification
- Bag of Visual Words
- Machine Vision
