The engine of innovative applications The application of AI in the semiconductor industry does not end there. In fact, the two complement each other.
Materials science is the driving force behind semiconductor progress. AI can accelerate the development of new materials, discover the inherent laws between material properties and synthesis conditions from a large amount of experimental data, and quickly select the best formula. The development of some novel materials, such as high-entropy alloys and metal-organic frameworks, has begun to adopt this “smart experiment” model.
Emerging process technologies
Such as extreme ultraviolet lithography, nanopsarinting, etc., have higher requirements on materials and process conditions. AI can Brother Cell Phone List establish accurate process models, opti mize each process parameter, and accelerate the introduction of new processes. ASsa ML’s virtual exposure machine combines AI and high-performance computing to verify and opti mize process design before hardware construction, significantly shortening the development cycle.
In supply chain management
AI can also integrate real-time Bahrain Phone Number List data on supply, demand and logistics, accurately prict changes in supply and demand, schule resources in advance, and create a resilient supply chain. TSMC uses AI algorithms to dynamically prict and schule wafer demand with an accuracy of over 85%, effectively improving production capacity utilization.
As semiconductor upstream technology continues to evolve, it can also drive continuous improvement in the user experience on the application side. semiconductor market. For example, Tesla’s FSD chip is specially optimiz for self-driving algorithms, and its computing performance is 21 times higher than that of GPUs. Hui da’s Grace CPU is built for massive AI calculations. A single chip can support memory bandwidths of up.Various mobile terminal chips have also add AI functions to inject new momentum into different applications.
