Bridging the Sim-to-Real Gap in Semiconductor Visual Program Synthesis via Input Binarization 事件

Bridging the Sim-to-Real Gap in Semiconductor Visual Program Synthesis via Input Binarization arXiv:2606.02434v1 Announce Type: new Abstract: Precise parametric control over circuit geometry is essential for semiconductor inspection, yet obtaining sufficient real training data remains costly. Although generative models such as diffusion models and Generative Adversarial Networks (GANs) can augment training data, they cannot guarantee the nanometer-scale geometric accuracy required for metrology