Machine learning–assisted global optimization of photonic devices

Zhaxylyk A Kudyshev,Vladimir M Shalaev,Alexander V Kildishev,Alexandra Boltasseva

doi:10.1515/nanoph-2020-0376

Zhaxylyk A Kudyshev, Vladimir M Shalaev + Show 2 more

Open Access

https://doi.org/10.1515/nanoph-2020-0376

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Abstract

AbstractOver the past decade, artificially engineered optical materials and nanostructured thin films have revolutionized the area of photonics by employing novel concepts of metamaterials and metasurfaces where spatially varying structures yield tailorable “by design” effective electromagnetic properties. The current state-of-the-art approach to designing and optimizing such structures relies heavily on simplistic, intuitive shapes for their unit cells or metaatoms. Such an approach cannot provide the global solution to a complex optimization problem where metaatom shape, in-plane geometry, out-of-plane architecture, and constituent materials have to be properly chosen to yield the maximum performance. In this work, we present a novel machine learning–assisted global optimization framework for photonic metadevice design. We demonstrate that using an adversarial autoencoder (AAE) coupled with a metaheuristic optimization framework significantly enhances the optimization search efficiency of the metadevice configurations with complex topologies. We showcase the concept of physics-driven compressed design space engineering that introduces advanced regularization into the compressed space of an AAE based on the optical responses of the devices. Beyond the significant advancement of the global optimization schemes, our approach can assist in gaining comprehensive design “intuition” by revealing the underlying physics of the optical performance of metadevices with complex topologies and material compositions.

Highlights

Multiconstrained optimization of metamaterials [1] and metasurfaces [2,3,4,5] requires intensive computational efforts
We demonstrate that using an adversarial autoencoder (AAE) coupled with a metaheuristic optimization framework significantly enhances the optimization search efficiency of the metadevice configurations with complex topologies
We have developed a global optimization framework utilizing a conditional AAE (c-AAE) network that can be applied to a wide range of highly constrained optimization problems in nanophotonics and plasmonics, as well as in biology, chemistry, and quantum optics

Summary

Introduction

Multiconstrained optimization of metamaterials [1] and metasurfaces [2,3,4,5] requires intensive computational efforts. Along with the optimization of geometrical parameters and prediction of the optical response of metastructures with simplistic shapes, the advanced deep learning algorithms have been used to perform optimization of the metadevices with complex topologies. We have developed a methodology to perform the multiparametric global optimization (GO) directly within the compressed design space, via coupling the conditional AAE (c-AAE) network with a differential evolution (DE) optimizer. We demonstrated that supervised training of the c-AAE network allows adding physics-driven regularization to compressed design space during the training phase, which in turn leads to better GO searches. To showcase the performance of the proposed AAE-based GO technique, we optimized the thermal emitter design with two different methods: (i) the c-AAE network coupled with a DE optimizer (c-AAE + DE) and (ii) DE optimization utilizing the compressed design space with physics-driven regularization (c-AAE + rDE).

Optimization problem

Conditional AAEs for rapid design generation

AAE-assisted global optimization

Physics-driven compressed space engineering

Findings

Conclusion