Journal
ACS PHOTONICS
Volume 5, Issue 4, Pages 1365-1369Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acsphotonics.7b01377
Keywords
nanophotonics; inverse scattering; neural networks
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Funding
- DARPA YFA program [YFA17 N66001-17-1-4049]
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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 nonuniqueness 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 nonunique electromagnetic scattering instances. This paves the way for using deep neural networks to design complex photonic structures that require large training data sets.
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