Self-supervised representation learning for interpretation of OCD data
US11747740B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 6, 2021 |
| Grant date | Sep 5, 2023 |
| Priority date | — |
| Expiry date | Jan 6, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG01N2201/1296
- WIPO fieldMeasurement
- WIPO sectorInstruments
Abstract
A system and methods for OCD metrology are provided including receiving multiple first sets of scatterometric data, dividing each set into k sub-vectors, and training, in a self-supervised manner, k2 auto-encoder neural networks that map each of the k sub-vectors to each other. Subsequently multiple respective sets of reference parameters and multiple corresponding second sets of scatterometric data are received and a transfer neural network (NN) is trained. Initial layers include a parallel arrangement of the k2 encoder neural networks. Target output of the transfer NN training is set to the multiple sets of reference parameters and feature input is set to the multiple corresponding second sets of scatterometric data, such that the transfer NN is trained to estimate new wafer pattern parameters from subsequently measured sets of scatterometric data.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.