PRESENTATION

Advanced Process Control for Rapid Scaling of Next Generation Materials

Process improvement for the manufacturing of effective high performance gallium oxide (Ga2O3) based semiconductor devices is imperative in consolidating Ga2O3 as a singularly promising material for cost effective, mass-producible, and robust manufacturing. Other wide bandgap silicon alternatives (i.e. SiC and GaN) are impeded by high costs and complicated, time-consuming adjustments. Enhancing growth rate of Ga2O3 films without compromising crystal quality can be accomplished through implementing insights offered by ancillary deep learning models. Artificial intelligence techniques that take programmed reactor settings, sensor-read environmental conditions, resulting crystallographic defectivity, and overall outgoing quality as inputs can infer processing improvements upstream using neural networks trained by skilled engineers.