Artificial Neural Networks to Predict Sheet Resistance of Indium-Doped Zinc Oxide Thin Films Deposited via Plasma Deposition

Journal article


Salimian, A., Aminishahsavarani, A. and Upadhyaya, H. (2022). Artificial Neural Networks to Predict Sheet Resistance of Indium-Doped Zinc Oxide Thin Films Deposited via Plasma Deposition. Coatings. 12 (2), p. 225. https://doi.org/10.3390/coatings12020225
AuthorsSalimian, A., Aminishahsavarani, A. and Upadhyaya, H.
Abstract

We implemented deep learning models to examine the accuracy of predicting a single
feature (sheet resistance) of thin films of indium-doped zinc oxide deposited via plasma sputter
deposition by feeding the spectral data of the plasma to the deep learning models. We carried out
114 depositions to create a large enough dataset for use in training various artificial neural network
models. We demonstrated that artificial neural networks could be implemented as a model that
could predict the sheet resistance of the thin films as they were deposited, taking in only the spectral
emission of the plasma as an input wit

Keywordsdeep learning; sputtering; TCO; plasma
Year2022
JournalCoatings
Journal citation12 (2), p. 225
PublisherMDPI
Digital Object Identifier (DOI)https://doi.org/10.3390/coatings12020225
Web address (URL)https://www.mdpi.com/2079-6412/12/2/225
Publication dates
Print09 Feb 2022
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Accepted06 Feb 2022
Deposited14 Feb 2022
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