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A validated framework that removes a memory wall and makes globally optimized freeform meta-optics practical, and substantially outperform meta-atom designs, reaching 1.64, 1.65 and 1.24 times their objectives.
Gradient-based photonic design needs full-wave derivatives with respect to millions of parameters, but on a single GPU workstation the time-domain adjoint is limited by device memory and by the incompatibility of fused update kernels with automatic differentiation. Here, we present TorchFDTD, an open-source finite-diff...
Full-wave electromagnetic simulations provide accurate field distributions for nanophotonic structures, but their high computational cost limits their direct use in large-scale inverse design. Here, we introduce a deep-learning-assisted inverse-design framework in which a neural network is trained with finite-differenc...
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