Inverse Design of Photonic Surfaces via High throughput Femtosecond Laser Processing and Tandem Neural Networks

Alok Singh, Puya Vahabi

Abstract:

This work demonstrates a method to design photonic surfaces by combining
femtosecond laser processing with the inverse design capabilities of tandem
neural networks that directly link laser fabrication parameters to their resulting
textured substrate optical properties. High throughput fabrication and
characterization platforms are developed that generate a dataset comprising
35280 unique microtextured surfaces on stainless steel with corresponding
measured spectral emissivities. The trained model utilizes the nonlinear
one-to-many mapping between spectral emissivity and laser parameters.
Consequently, it generates predominantly novel designs, which reproduce the
full range of spectral emissivities (average root-mean-squared-error < 2.5%)
using only a compact region of laser parameter space 25 times smaller than
what is represented in the training data. Finally, the inverse design model is
experimentally validated on a thermophotovoltaic emitter design application.
By synergizing laser-matter interactions with neural network capabilities, the
approach offers insights into accelerating the discovery of photonic surfaces,
advancing energy harvesting technologies.