Measurement recipe optimization based on probabilistic domain knowledge and physical realization
US11520321B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 7, 2020 |
| Grant date | Dec 6, 2022 |
| Priority date | — |
| Expiry date | Jun 10, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T7/0004
- WIPO fieldControl
- WIPO sectorInstruments
Abstract
Methods and systems for training and implementing metrology recipes based on performance metrics employed to quantitatively characterize the measurement performance of a metrology system in a particular measurement application. Performance metrics are employed to regularize the optimization process employed during measurement model training, model-based regression, or both. For example, the known distributions associated with important measurement performance metrics such as measurement precision, wafer mean, etc., are specifically employed to regularize the optimization that drives measurement model training. In a further aspect, a trained measurement model is employed to estimate values of parameters of interest based on measurements of structures having unknown values of one or more parameters of interest. In a further aspect, trained measurement model performance is validated with test data using error budget analysis. In another aspect, a model-based regression on a measurement model is physically regularized by on one or more measurement performance metrics.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.