5 years ago

Training deep neural networks for the inverse design of nanophotonic structures.

Yixuan Tan, Dianjing Liu, Zongfu Yu

Data inconsistency leads to a slow training process when deep neural networks are used for the inverse design of photonic devices, an issue that arises from the fundamental property of non-uniqueness in all inverse scattering problems. Here we show that by combining forward modeling and inverse design in a tandem architecture, one can overcome this fundamental issue, allowing deep neural networks to be effectively trained by data sets that contain non-unique electromagnetic scattering instances. This paves the way for using deep neural networks to design complex photonic structures that requires large training sets.

Publisher URL: http://arxiv.org/abs/1710.04724

DOI: arXiv:1710.04724v2

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