{"@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 \u03bb 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": [{"downloadURL": "https://data.nist.gov/od/ds/6388F53FD1DBB474E0531A57068183FF1887/sourcecode.zip", "mediaType": "application/zip"}, {"downloadURL": "https://data.nist.gov/od/ds/6388F53FD1DBB474E0531A57068183FF1887/sourcecode.zip.sha256", "mediaType": "text/plain"}, {"accessURL": "https://doi.org/10.18434/T4/1502429", "description": "DOI Access to Model-Based Optical Metrology in R: M.o.R.", "format": "text/html", "title": "DOI Access to Model-Based Optical Metrology in R: M.o.R."}, {"downloadURL": "https://data.nist.gov/od/ds/6388F53FD1DBB474E0531A57068183FF1887/MoR_Documentation.pdf", "mediaType": "application/pdf"}, {"downloadURL": "https://data.nist.gov/od/ds/6388F53FD1DBB474E0531A57068183FF1887/MoR_Documentation.pdf.sha256", "mediaType": "text/plain"}], "identifier": "6388F53FD1DBB474E0531A57068183FF1887", "keyword": ["statistics", "model-based metrology"], "landingPage": "https://data.nist.gov/od/id/6388F53FD1DBB474E0531A57068183FF1887", "language": ["en"], "license": "https://www.nist.gov/open/license", "modified": "2018-01-24", "programCode": ["006:045"], "publisher": {"@type": "org:Organization", "name": "National Institute of Standards and Technology"}, "theme": ["Mathematics and Statistics:Uncertainty quantification", "Mathematics and Statistics:Numerical methods and software", "Mathematics and Statistics:Statistical analysis"], "title": "Model-Based Optical Metrology in R: M.o.R."}