Simulator-based training of generative neural networks for the inverse design of metasurfaces

Jonathan A Fan,Jiaqi Jiang

doi:10.1515/nanoph-2019-0330

Abstract

Abstract Metasurfaces are subwavelength-structured artificial media that can shape and localize electromagnetic waves in unique ways. The inverse design of these devices is a non-convex optimization problem in a high dimensional space, making global optimization a major challenge. We present a new type of population-based global optimization algorithm for metasurfaces that is enabled by the training of a generative neural network. The loss function used for backpropagation depends on the generated pattern layouts, their efficiencies, and efficiency gradients, which are calculated by the adjoint variables method using forward and adjoint electromagnetic simulations. We observe that the distribution of devices generated by the network continuously shifts towards high performance design space regions over the course of optimization. Upon training completion, the best generated devices have efficiencies comparable to or exceeding the best devices designed using standard topology optimization. Our proposed global optimization algorithm can generally apply to other gradient-based optimization problems in optics, mechanics, and electronics.

Highlights

Photonic technologies serve to manipulate, guide, and filter electromagnetic waves propagating in free space and in waveguides
We present a detailed mathematical discussion of a new global optimization concept based on Global Topology Optimization Networks (GLOnets) [21], which combine adjoint variables electromagnetic calculations with the training of a generative neural network to realize high performance photonic structures
The efficiencies of the best devices designed using gradient-based topology optimization and GLOnets are shown in Figure 4. 90% of the best devices from GLOnets have higher or the same efficiencies compared to the best devices produced from gradient-based topology optimization. 98% of the best devices from GLOnets have efficiencies within 5% of the best devices from gradient-based topology optimization

Summary

Introduction

Photonic technologies serve to manipulate, guide, and filter electromagnetic waves propagating in free space and in waveguides. Silicon photonic devices typically utilize adiabatic tapers and ring resonators to route and filter guided waves [1], and metasurfaces, which are diffractive optical components used for wavefront engineering, typically utilize arrays of nanowaveguides or nanoresonators comprising simple shapes [2]. While these design concepts work well for certain applications, they possess limitations, such as narrow bandwidths and sensitivity to temperature, which prevent the further advancement of these technologies. The identification of a high performance device is computationally expensive, as it requires the optimization of multiple random initial dielectric distributions and selecting the best device

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Conclusion