Publications Details

Project Final Report: Machine Learning of Plasma Science for Next Generation Microelectronics

Bentz, Brian Z.; Hardin, Thomas J.; Fierro, Andrew S.; Youngman, Kevin; Barberena Valencia, Juan P.; Hopkins, Matthew M.; Gorman, Grant M.; Belianinov, Alex A.

Low temperature plasmas (LTPs) are an enabling technology behind reducing device dimensions and the continuation of Moore’s Law. It is estimated that 40-45% of all process steps necessary to manufacture semiconductor devices involve LTPs [4]. However, challenges in plasma process design and continuous incorporation of novel materials for new device architectures are pushing the limits of what is possible with current plasma technology. For example, creating higher aspect ratio structures and etching features at the atomic scale both require finer control of the ion energy/velocity at wafer surfaces. To support these types of future innovations in the plasma processing systems that Sandia and the DOE rely upon, we have developed novel diagnostics, simulations, and machine learning capabilities to discover, characterize, and predict plasma phenomena affecting the ion energy/velocity distribution function (IEDF). These efforts also supported research program devel opment and external collaboration with industry and academia through Sandia’s Plasma Research Facility (PRF). This report will focus on the following topics and accomplishments of this three year LDRD project, briefly summarized.