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Description

The objective of the project is to employ AI tools such as machine learning (ML) and deep learning (DL) to predict the sheet resistance of titanium oxynitride (TiON) thin films based on input features such as deposition parameters, film composition and thickness. Sheet resistance is a critical property for thin films in electronic applications. The complex relationships between material properties and deposition parameters calls for a shift from the traditional paradigm of experimental science to the modern paradigm of data exploration. The research focuses on the growth of high-quality titanium oxynitride thin films, using pulsed laser deposition (PLD) method, which allows control of growth parameters, leading to formation of high quality epitaxial thin films with precise control of its electrocatalytic properties. The films are used as catalysts to examine the electrochemical reactions during water splitting, with an aim of producing hydrogen, which is a source of renewable energy. MATLAB regression learner App together with experimental data has been used to train the models. The utilization of AI tools, along with the algorithmic processing of experimental data, has the potential to support data analysis and it can facilitate the systematic correlation of material structure and properties.

Publication Date

4-1-2025

Keywords

Thin films, Machine Learning, Deep Learning, Pulsed Laser Deposition

Predicting the Sheet Resistance of Titanium Oxynitride Thin Films using AI Tools-Machine and Deep Learning

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