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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【Case Summary】 ■The number of experiments was reduced to 19. ■Processing accuracy was achieved that allowed for the omission of subsequent processes, exceeding the target. ■No additional capital investment was required. ■A combination that had never been tried by engineers. *For more details, please refer to the PDF document or feel free to contact us.
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For more details, please refer to the PDF document or feel free to contact us.
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AICrystal Inc. is a startup aiming to revitalize the manufacturing industry by providing appropriate services tailored to our customers' technical challenges and phases of technology development, enabling the creation of products with overwhelming added value using process informatics technology. Through our unique process informatics technology, which does not solely focus on data science, we aim to realize data-driven new manufacturing by offering various services such as education, data acquisition support, analysis services, and applications. We can achieve a development process that is faster and more efficient compared to traditional methods. Our core technology is not only developed in-house but is also being advanced through national projects in collaboration with Nagoya University and RIKEN. To expand the technology and know-how accumulated in solving numerous manufacturing challenges to more customers, we are developing and providing our own SaaS products.
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