Abstract

Inverse design of photonic nanostructures based on machine learning (ML) methods has recently attracted much attention. An artificial neural network (ANN) showcases good performance on the photonic inverse design problem. However, there are still many unsolved issues regarding an efficient way to find a geometry that yields the target response by data-driven ML approaches. The design of air mass (AM) 1.5G filters for solar simulators represents such a challenging case. Here, we propose and show that a recurrent neural adjoint method is efficient in optimizing a multilayer optical filter for that purpose. Two examples of inverse design and optimization for an AM 1.5G filter with ${{\rm Si}_{3}} {{\rm N}_{4}} / {{\rm SiO}_{2}}$ and (${{\rm Si}_{3}} {{\rm N}_{4}} / {{\rm SiO}_{2}}$)/(${{\rm Ta}_{2}} {{\rm O}_{5}} / {{\rm SiO}_{2}}$) films at a different spectrum band (e.g.,  $\lambda = 280\;{\rm nm} - 800\;{\rm nm}$ and $\lambda = 280\;{\rm nm} - 1350\;{\rm nm}$) have been demonstrated. By comparing several strategies based on ANN approaches, a generic and efficient scheme is presented for photonic multilayer film structure engineering, which we believe could be applied to various photonic device designs.

© 2021 Optical Society of America

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