*Joe Brindley, Benoit Daniel, Victor Bellido-Gonzalez, Dermot Monaghan, Gencoa Ltd, Liverpool, United Kingdom
Vacuum deposition processes are being equipped with an ever-expanding array of sensors to gain more control over the process conditions. Unfortunately, this often presents the machine operator with too much data to be able to draw clear insights into the performance of the process. Machine learning algorithms are a powerful tool for analyzing large and complex sets of data and have been at the forefront of a revolution artificial intelligence. These techniques are ideally suited for analyzing problems encountered in vacuum processes, which are often expressed as “classification problems”, i.e., identifying if a leak is present in the system or not. In particular, they can be applied to the automated analysis of optical emission spectra (OES) of plasma. OES provides critical information on the state or condition of a process. However, expert knowledge is often required to be able to interpret the data, and in some cases, the spectra are too complex to extract key information using the human eye alone. This paper will present the application of a machine learning A.I. to the automated analysis of magnetron and remote plasma OES data. Examples include leak detection, organic contamination detection and the identification of organic molecules from cracking patterns.