High-Accuracy Prediction of ScAlN Thin Film Dry Etching Using Machine Learning Driven Regression Modeling

Scandium aluminum nitride (ScAlN) demonstrates notable properties such as high electromechanical coupling, low dielectric permittivity, reduced acoustic losses, and so on. Yet, ScAlN-based device fabrication is challenging, especially controlling the etching process at higher scandium concentrations. This study optimizes the etch recipe by precisely controlling the ScAlN etch rate and sidewall angle using an inductively coupled plasma (ICP) system, characterizing the results with scanning electron microscopy (SEM). We acknowledged that the etch gas flow rates, ICP/RF power, and chamber pressure critically influence the ScAlN etch rate and sidewall angle. Implementing a machine learning (ML) regression model, we predicted these etching outcomes as a function of etch parameters based on historical data correlations, accounting for complex underlying etching physics. We validated the ML model using an independent data set, and it demonstrated over 95% accuracy in predicting ScAlN etch characteristics. This approach significantly enhances the understanding of ScAlN etching, proving ML’s efficiency in etch recipe optimization, which is essential for fabrication of advanced ScAlN-based devices in optoelectronics and RF applications.