{
  "@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": [
    {
      "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"
    },
    {
      "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"
    }
  ],
  "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": [
    "006:045"
  ],
  "publisher": {
    "@type": "org:Organization",
    "name": "National Institute of Standards and Technology"
  },
  "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."
}