Significantly reduce calculation time! Introducing case studies of process informatics!
"GaN (gallium nitride)" is a power semiconductor that is expected to be adopted as a power source for 5G base stations. It is commonly used as a thin sliced disk wafer, but the initial manufacturing state is cylindrical. The GaN cylinder emits gas, which serves as the raw material for GaN, from holes at the bottom, allowing the crystals to grow. In reality, there are over 100 holes, leading to infinite combinations. When simulating this, it takes an astonishing 6 hours for just one condition. If you run 1000 variations, it consumes 6000 hours, which equals 250 days, taking a significant amount of time. This is where "Process Informatics" from AICrystal comes into play. By training AI with the results of 1000 randomly generated simulations, the calculation time is drastically reduced. The time required is just 1 second per condition. Thanks to the significant reduction in calculation time enabled by AI, it is now possible to compute 10,000 conditions in just about 3 hours. This optimization allows for the gas conditions and hole arrangements to be fine-tuned, successfully tripling the growth rate. *For more details, please refer to the PDF document or feel free to contact us.*
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basic information
【Case Summary】 ■ By training AI with 1,000 randomly generated simulation results, we significantly reduced computation time. ■ Only 1 second per condition. ■ Successfully optimized gas conditions and hole arrangements, tripling the growth rate. *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.
Company information
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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