  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.
