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Model-Based Optical Metrology in R: M.o.R.
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.
Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| accrualPeriodicity | irregular |
| bureauCode |
[
"006:55"
]
|
| contactPoint |
{
"fn": "Mark Alexander Henn",
"hasEmail": "mailto:mark.henn@nist.gov"
}
|
| describedBy | https://mahenn.shinyapps.io/MoR1/ |
| 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. |
| distribution |
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"title": "DOI Access to Model-Based Optical Metrology in R: M.o.R.",
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| identifier | 6388F53FD1DBB474E0531A57068183FF1887 |
| keyword |
[
"model-based metrology",
"statistics"
]
|
| landingPage | https://data.nist.gov/od/id/6388F53FD1DBB474E0531A57068183FF1887 |
| language |
[
"en"
]
|
| license | https://www.nist.gov/open/license |
| modified | 2018-01-24 |
| programCode |
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]
|
| publisher |
{
"name": "National Institute of Standards and Technology",
"@type": "org:Organization"
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|
| theme |
[
"Mathematics and Statistics:Numerical methods and software",
"Mathematics and Statistics:Statistical analysis",
"Mathematics and Statistics:Uncertainty quantification"
]
|
| title | Model-Based Optical Metrology in R: M.o.R. |
