, Volume 67, Issue 1, pp 154–163 | Cite as

A Semi-Empirical Model for Tilted-Gun Planar Magnetron Sputtering Accounting for Chimney Shadowing

  • J. K. Bunn
  • C. J. Metting
  • J. Hattrick-SimpersEmail author


Integrated computational materials engineering (ICME) approaches to composition and thickness profiles of sputtered thin-film samples are the key to expediting materials exploration for these materials. Here, an ICME-based semi-empirical approach to modeling the thickness of thin-film samples deposited via magnetron sputtering is developed. Using Yamamura’s dimensionless differential angular sputtering yield and a measured deposition rate at a point in space for a single experimental condition, the model predicts the deposition profile from planar DC sputtering sources. The model includes corrections for off-center, tilted gun geometries as well as shadowing effects from gun chimneys used in most state-of-the-art sputtering systems. The modeling algorithm was validated by comparing its results with experimental deposition rates obtained from a sputtering system utilizing sources with a multi-piece chimney assembly that consists of a lower ground shield and a removable gas chimney. Simulations were performed for gun-tilts ranging from 0° to 31.3° from the vertical with and without the gas chimney installed. The results for the predicted and experimental angular dependence of the sputtering deposition rate were found to have an average magnitude of relative error of \( 4.14\% \pm 3.02\% \) for a 0°–31.3° gun-tilt range without the gas chimney, and \( 2.12\% \pm 1.71\% \) for a 17.7°–31.3° gun-tilt range with the gas chimney. The continuum nature of the model renders this approach reverse-optimizable, providing a rapid tool for assisting in the understanding of the synthesis-composition-property space of novel materials.


Deposition Rate Monte Carlo Maximum Relative Error Integrate Computational Material Engineering Material Genome Initiative 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The work is supported by the South Carolina SmartState Center of Economic Excellence for Strategic Approaches to the Generation of Electricity (SAGE).

Supplementary material

11837_2014_1234_MOESM1_ESM.pdf (16.9 mb)
Supplementary material 1 (PDF 17296 kb)


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Copyright information

© The Minerals, Metals & Materials Society 2014

Authors and Affiliations

  • J. K. Bunn
    • 1
  • C. J. Metting
    • 1
  • J. Hattrick-Simpers
    • 1
    Email author
  1. 1.Chemical EngineeringUniversity of South CarolinaColumbiaUSA

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