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[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 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.*

  • Other analyses
  • Analysis Services
  • Other services

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Eliminate waste in production lines and construction plans! Combination alignment AI solution.

Presenting case studies! Achieving alignment in production processes and civil engineering construction plans, including routes and deliveries! AI autonomously selects the most suitable options from combinations!

The "Combination Matching Solution" is a solution aimed at solving the "combination matching problem," which involves selecting the best option from a large number of choices generated by combinations of elements, using reinforcement learning. It is expected to be utilized in various applications such as manufacturing line process scheduling, delivery route selection, construction planning, and cargo placement planning. Please feel free to contact us if you have any inquiries. 【Benefits of Reinforcement Learning × Optimization】 ■ Even if the matching problem becomes large, reinforcement learning tends to keep the inference time required for optimization from increasing significantly. ■ It is possible to construct algorithms for various matching problems within almost a single framework. *For more details, please download the PDF or feel free to contact us.

  • Software (middle, driver, security, etc.)

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