Machine-Learning-Assisted Metasurface Design for High-Efficiency Thermal Emitter Optimization

TL;DR

This paper introduces a machine learning-enhanced topology optimization method for designing high-efficiency thermal metasurfaces, significantly reducing computational costs and expanding design possibilities in complex, constrained nanophotonic systems.

Contribution

It combines topology optimization with adversarial autoencoders to improve control over design space, enabling efficient global searches in high-dimensional, constrained optimization problems.

Findings

Enhanced optimization efficiency and control over design space.

Potential for broader applications beyond photonics.

Improved design capabilities for complex, multi-constrained systems.

Abstract

With the emergence of new photonic and plasmonic materials with optimized properties as well as advanced nanofabrication techniques, nanophotonic devices are now capable of providing solutions to global challenges in energy conversion, information technologies, chemical/biological sensing, space exploration, quantum computing, and secure communication. Addressing grand challenges poses inherently complex, multi-disciplinary problems with a manifold of stringent constraints in conjunction with the required system's performance. Conventional optimization techniques have long been utilized as powerful tools to address multi-constrained design tasks. One example is so-called topology optimization that has emerged as a highly successful architect for the advanced design of non-intuitive photonic structures. Despite many advantages, this technique requires substantial computational resources and thus has very limited applicability to highly constrained optimization problems within high-dimensions parametric space. In our approach, we merge the topology optimization method with machine learning algorithms such as adversarial autoencoders and show substantial improvement of the optimization process by providing unparalleled control of the compact design space representations. By enabling efficient, global optimization searches within complex landscapes, the proposed compact hyperparametric representations could become crucial for multi-constrained problems. The proposed approach could enable a much broader scope of the optimal designs and data-driven materials synthesis that goes beyond photonic and optoelectronic applications.