We introduce a fully unsupervised deep-learning method for inverse design of nanophotonics using dimension expansion to eliminate pre-generated datasets and post-processing. The effectiveness of the approach is validated on two distinct designs: Y-splitters and metalenses.
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Huang et al. (2025) studied this question.
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