Methods and systems for predicting silicon density for a metal layer of semi-conductor chip via machine learning
US12307184B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 21, 2023 |
| Grant date | May 20, 2025 |
| Priority date | — |
| Expiry date | Sep 21, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F2119/18
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A specification for a semi-conductor chip is received. The specification specifies a set of photomasks associated with a metal layer of the semi-conductor chip. Multiple portions of an area of the metal layer are identified. A respective image is generated for each portion of the area based on the photomasks. A respective drawn density of metal wires for each portion of the area is calculated. A trained machine learning model is invoked to predict a respective silicon density of metal wires for each respective portion of the area based on an image and a drawn density for the respective portion of the area. A silicon density for the area of the metal layer is calculated based on a combination of predicted silicon densities for the multiple portions of the area. The combination is based on an average value of the predicted silicon densities for the multiple portions of the area.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.