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Process improvement for the manufacture 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. Beginning with a bandgap of 4.7eV, Ga2O3 offers an unparalleled solution when growth parameters are tuned and controlled using deep reinforcement learning agents. Ga2O3 wafer production employs (non-exclusively) the scalable and cost-effective Czochralski method for ingot growth and MOCVD process for epitaxy growth, making it a viable candidate for high volume commercial radiofrequency device manufacture. As crystal quality and electron transport depend on reactor temperature, vertical gas and precursors flows, chamber pressure, and a host of kinetic parameters during growth, it follows that the configuration space for Ga2O3 deposition is expansive and costly to explore. 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. My stipulations call for a hardware accelerated deep learning controller (DLC) that digests the multimedia output of reactors, MOCVD systems, and metrology tools to optimize Ga2O3 crystal quality and ultimately increase die yields, reduce waste, accelerate product development, decrease time to market, and eliminate need for labor-exhaustive testing.
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