Substrate process endpoint detection using machine learning
US11901203B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 10, 2021 |
| Grant date | Feb 13, 2024 |
| Priority date | — |
| Expiry date | Nov 14, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/10
- WIPO fieldMeasurement
- WIPO sectorInstruments
Abstract
Methods and systems for detection of an endpoint of a substrate process are provided. A set of machine learning models are trained to provide a metrology measurement value associated with a particular type of metrology measurement for a substrate based on spectral data collected for the substrate. A respective machine learning model is selected to be applied to future spectral data collected during a future substrate process for a future substrate in view of a performance rating associated with the particular type of metrology measurement. Current spectral data is collected during a current process for a current substrate and provided as input to the respective machine learning model. An indication of a respective metrology measurement value corresponding to the current substrate is extracted from one or more outputs of the trained machine learning model. In response to a determination that the respective metrology measurement satisfies a metrology measurement criterion, an instruction including a command to terminate the current process is generated.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.