Name
Abstract | ||
|---|---|---|
In the semiconductor industry, reverse engineering is used to extract information from microchips. Circuit extraction is becoming increasingly difficult due to the continuous technology shrinking. A high quality reverse engineering process is challenged by various defects coming from chip preparation and imaging errors. Currently, no automated, technology-agnostic defect inspection framework is available. To meet the requirements of the mostly manual reverse engineering process, the proposed automated framework needs to handle highly imbalanced data, as well as unknown and multiple defect classes. We propose a network architecture that is composed of a shared Xception-based feature extractor and multiple, individually trainable binary classification heads: the HydREnet. We evaluated our defect classifier on three challenging industrial datasets and achieved accuracies of over 85 %, even for underrepresented classes. With this framework, the manual inspection effort can be reduced down to 5 %. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/WACV51458.2022.00187 | 2022 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2022) |
DocType | ISSN | Citations |
Conference | 2472-6737 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ann-Christin Bette | 1 | 0 | 0.34 |
| Patrick Brus | 2 | 0 | 0.34 |
| Gábor Balázs | 3 | 0 | 0.68 |
| Matthias Ludwig | 4 | 0 | 0.34 |
| Alois Knoll Knoll | 5 | 1700 | 271.32 |