AI Driven Laser Parameter Search: Inverse Design of Photonic Surfaces using Greedy Surrogate-based Optimization

TL;DR

This paper introduces a surrogate-based optimization method using Random Forests and greedy exploration to efficiently design photonic surfaces with desired optical properties, outperforming existing algorithms.

Contribution

The study presents a novel surrogate-based inverse design framework employing Random Forests and a greedy strategy, with a warm-start technique for changing targets.

Findings

Superior performance over other algorithms on benchmarks

Effective inverse design of photonic surfaces

Warm-start technique improves adaptability

Abstract

Photonic surfaces designed with specific optical characteristics are becoming increasingly important for use in in various energy harvesting and storage systems. , In this study, we develop a surrogate-based optimization approach for designing such surfaces. The surrogate-based optimization framework employs the Random Forest algorithm and uses a greedy, prediction-based exploration strategy to identify the laser fabrication parameters that minimize the discrepancy relative to a user-defined target optical characteristics. We demonstrate the approach on two synthetic benchmarks and two specific cases of photonic surface inverse design targets. It exhibits superior performance when compared to other optimization algorithms across all benchmarks. Additionally, we demonstrate a technique of inverse design warm starting for changed target optical characteristics which enhances the performance of the introduced approach.