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Princeton researchers use reinforcement learning and inverse design to rapidly create radio-frequency integrated circuits from scratch. Diffusion models generate novel RF layouts that achieve record performance and drastically reduce design time. Future progress requires large shared chip design datasets and open ecosystems for AI to learn universal electromagnetic behaviors.
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