Data provided by National Institute of Standards and Technology
Reliable optical critical dimension (OCD) metrology in the regime where the inspection wavelength λ is much larger than the critical dimensions (CDs) of the measurand is only possible using a model-based approach. Due to the complexity of the models involved, that often require solving Maxwell's equations, many applications use a library based look-up approach. Here, the best experiment-to-theory fit is found by comparing the measurement data to a library consisting of pre-calculated simulations. One problem with this approach is that it makes the accuracy of the solution dependent on the refinement of the grid. Interpolating between library values requires a uniform grid in most cases, and can also be very time-consuming. We present an approach based on radial basis functions that is fast, accurate and most importantly works on arbitrary grids. The method is implemented in a application based on the programming language R, that additionally allows for Bayesian data analysis, and provides multiple diagnostics.
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Updated: 2026-09-19
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2018-01-24
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| Title | Model-Based Optical Metrology in R: M.o.R. |
|---|---|
| Description | Reliable optical critical dimension (OCD) metrology in the regime where the inspection wavelength λ is much larger than the critical dimensions (CDs) of the measurand is only possible using a model-based approach. Due to the complexity of the models involved, that often require solving Maxwell's equations, many applications use a library based look-up approach. Here, the best experiment-to-theory fit is found by comparing the measurement data to a library consisting of pre-calculated simulations. One problem with this approach is that it makes the accuracy of the solution dependent on the refinement of the grid. Interpolating between library values requires a uniform grid in most cases, and can also be very time-consuming. We present an approach based on radial basis functions that is fast, accurate and most importantly works on arbitrary grids. The method is implemented in a application based on the programming language R, that additionally allows for Bayesian data analysis, and provides multiple diagnostics. |
| Modified | 2018-01-24 |
| Publisher Name | National Institute of Standards and Technology |
| Contact | mailto:[email protected] |
| Keywords | statistics , model-based metrology |
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