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

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 wi