Machine learning based defect examination for semiconductor specimens
US12423800B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 4, 2023 |
| Grant date | Sep 23, 2025 |
| Priority date | — |
| Expiry date | Dec 19, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20084
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
There is provided a system and method of examination a semiconductor specimen. The method includes obtaining a runtime image of the specimen; processing the runtime image using a first machine learning (ML) model to extract a set of runtime features representative of a set of patches in the runtime image; and comparing the set of runtime features with a bank of reference features, giving rise to an anomaly map indicative of one or more defective patches in the runtime image. The bank of reference features is previously generated by obtaining a plurality of synthetic reference images generated by a second ML model based on a plurality of actual images; and processing the plurality of synthetic reference images by the first ML model to extract, for each synthetic reference image, a set of reference features representative thereof, giving rise to the bank of reference features.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.