| Abstract Scope |
Single crystal materials are the functional components in a variety of applications including energy, semiconductor, medical imaging, nuclear detection, electrification, and telecommunication. The demands for crystal materials are rising, driving an increase in specifications that require higher levels of testing, design, data analysis, and operation. However, traditional data-driven AI methods are not effective due to the long cycle times - days to months, high cost of experimentation - high-temperature, precious metals, and rare-earth elements, and complexity of the crystal growth processes – multidisciplinary, many control parameters and measured variables. Here we have developed a deep-tech approach to combine physical domain knowledge and ML models. This benchmark study on industrial data validates the predictive capabilities of physics-informed ML for use in crystal growth manufacturing, including melt growth, solid-state synthesis, vapor deposition, and epitaxy. The methods developed in this work enable data-driven decision-making to increase yield, optimize parameters, and reduce crystal defects. |