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 smallest roughness at the nanometer level (one-millionth of a millimeter) can affect performance, making crystal processing after creating a cylinder essential in traditional methods. To find appropriate experimental conditions, it is necessary to conduct experiments with at least two different conditions for each factor, meaning that for five factors, a minimum of 32 experiments is required. Based on those trends, we try dozens of experimental conditions to find the combination of factors that leads to the desired results. Therefore, traditional optimization methods centered around 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 later processes, and the benefits of not needing additional capital investment are significant. The conditions deemed appropriate here were combinations that had never been tried by engineers, showcasing 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 aimed at revitalizing the manufacturing industry by providing appropriate services tailored to our customers' technical challenges and phases, enabling the development 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. This enables 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 the RIKEN research institute. 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.
