Method of deep learning-based examination of a semiconductor specimen and system thereof
US12183066B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 8, 2021 |
| Grant date | Dec 31, 2024 |
| Priority date | — |
| Expiry date | Sep 17, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V10/82
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A computerized system and method of training a deep neural network (DNN) is provided. The DNN is trained in a first training cycle using a first training set including first training samples. Each first training sample includes at least one first training image synthetically generated based on design data. Upon receiving a user feedback with respect to the DNN trained using the first training set, a second training cycle is adjusted based on the user feedback by obtaining a second training set including augmented training samples. The DNN is re-trained using the second training set. The augmented training samples are obtained by augmenting at least part of the first training samples using defect-related synthetic data. The trained DNN is usable for examination of a semiconductor specimen.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.