Biomimetic Ultra-Broadband Perfect Absorbers Optimised with Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning approach to design biomimetic, ultra-broadband perfect absorbers inspired by moths' eyes, achieving over 90% absorption across a wide spectrum with high efficiency.

Contribution

It presents a novel application of double deep Q-learning to optimize complex optical structures, enabling rapid transfer of learned knowledge across different materials.

Findings

Achieved over 90% average absorption from 400 to 1600 nm.

Efficiently optimized designs from around 1 billion options.

Transferred learned policies to new materials, reducing optimization time.

Abstract

By learning the optimal policy with a double deep Q-learning network, we design ultra-broadband, biomimetic, perfect absorbers with various materials, based the structure of a moths eye. All absorbers achieve over 90% average absorption from 400 to 1,600 nm. By training a DDQN with motheye structures made up of chromium, we transfer the learned knowledge to other, similar materials to quickly and efficiently find the optimal parameters from the around 1 billion possible options. The knowledge learned from previous optimisations helps the network to find the best solution for a new material in fewer steps, dramatically increasing the efficiency of finding designs with ultra-broadband absorption.