Machine learning-based classification of defects in a semiconductor specimen
US12361531B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 24, 2020 |
| Grant date | Jul 15, 2025 |
| Priority date | — |
| Expiry date | Mar 19, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V2201/06
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
There is provided a method of automated defects' classification, and a system thereof. The method comprises obtaining data informative of a set of defects' physical attributes usable to distinguish between defects of different classes among the plurality of classes; training a first machine learning model to generate, for the given defect, a multi-label output vector informative of values of the physical attributes, thereby generating for the given defect a multi-label descriptor; and using the trained first machine learning model to generate multi-label descriptors of the defects in the specimen. The method can further comprise obtaining data informative of multi-label data sets, each data set being uniquely indicative of a respective class of the plurality of classes and comprising a unique set of values of the physical attributes; and classifying defects in the specimen by matching respectively generated multi-label descriptors of the defects to the multi-label data sets.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.