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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 with the objective of taking a step toward digital manufacturing in this area of material engineering.
Highlights
Transparent conductive oxides (TCO) are materials which have attracted a significant amount of attention due to their vast application areas
This study focused on the spectral emissions of the plasma, using them to predict the properties of the thin films deposited via the process
With modern computing powers and artificial intelligence (AI)-based data assessment methodologies, we aimed to explore implementing an alternative approach in plasma diagnostics during the sputter deposition process to assess the qualitative parameters of thin films as they were deposited
Summary
Transparent conductive oxides (TCO) are materials which have attracted a significant amount of attention due to their vast application areas. Researchers who apply this technique for TCO preparation usually report their findings by stating the condition of the sputtering process, such as chamber pressure, plasma power and the gas composition of the chamber during deposition. To achieve a particular thin film with certain features and functionality, multiple trial and error experimental runs are required to fine tune a machine to produce a specific desired coating. This means that, to fully digitize the sputter deposition process, significant constrains will arise. The diagnosis of laboratory plasma is usually carried out by optical emission spectroscopy (OES), through which numerous analytical techniques are established to determine certain plasma properties, such as electron density, plasma temperature, element recognition and qualification of elements present in the plasma
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