Process (computing)

Philosophy

Materials science

Operating system

Inverse

Computer science

Nanophotonics

Photonics

Physics

Nanotechnology

Mathematics

Optics

Epistemology

Mathematical analysis

Artificial intelligence

Geometry

Uniqueness

Deep learning

Inverse problem

Artificial neural network

Property (philosophy)

ACS Photonics

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.

Training Deep Neural Networks for the Inverse Design of Nanophotonic Structures