[Case Study on Process Informatics] Optimization of GaN Crystal Processing

Processing accuracy that allows for the omission of post-processing, exceeding the target! A case that demonstrates the value of collaboration between AI and humans!

In power semiconductors like GaN, even the slightest roughness on the nanometer scale (one-millionth of a millimeter) can affect performance, making crystal processing after creating the cylinder essential in traditional methods. To find appropriate experimental conditions, at least two sets of conditions must be tested for each factor just to observe trends. Therefore, for five factors, a minimum of 32 experiments is required. Based on those trends, dozens of experimental conditions are tested to find the combination of factors that leads to the desired results. As a result, traditional optimization methods centered on experiments required a significant number of trials. In contrast, the approach taken by Aicrystal, which learns from results, explores, and suggests conditions, was able to reduce the number of experiments to just 19. Moreover, this approach achieved a level of processing precision that allowed for the omission of subsequent processes, and the benefits of not needing additional capital investment are significant. The conditions deemed appropriate in this case were combinations that had never been tried by engineers, illustrating the value of collaboration between AI and humans. *For more details, please refer to the PDF document or feel free to contact us.*

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