ABSTRACT
Prominent tools needed for ALD experiments are ALD reactors incorporated with precise controls for gas flow, temperature, and pressure controllers. More insights from the growth mechanisms, structure property correlations, and functionality of thin films produced can be gained by deploying a machine learning-based predictive analysis using the data extracted from the ALD experiments. The information used in ALD experiments and simulations might originate from a variety of places, including experimental measurements, computer simulations, and published sources. It is crucial to prepare the ALD data from different sources in a structured way in order to make sure that they are easily accessible for predictive modeling based on machine learning. Using supervised learning techniques on input-output data from ALD experiments or simulations, precise predictive models that may be utilized to enhance ALD procedures and modify thin film properties to meet particular demands have been created.
