On-Demand Inverse Design for Narrowband Nanophotonic Structures Based on Generative Model and Tandem Network

TL;DR

This paper introduces a combined CVAE and tandem network framework for robust, on-demand inverse design of nanophotonic structures, effectively handling complex spectra and one-to-many mapping challenges.

Contribution

It presents a novel integrated architecture that adjusts target spectra and predicts structures, improving inverse design accuracy and practicality in nanophotonics.

Findings

Achieves high accuracy in spectral and structural predictions.

Effectively handles narrowband and complex spectra.

Validated with close match to full-wave simulations.

Abstract

Inverse design in nanophotonics remains challenging due to its ill-posed nature and sensitivity to input inaccuracies. We present a novel framework that combines a Conditional Variational Autoencoder (CVAE) with a tandem network, enabling robust and efficient on-demand inverse design of photonic structures. Unlike prior approaches that use CVAEs or tandem networks in isolation, our method integrates spectral adjustment and structural prediction in a unified architecture. Specifically, the CVAE adjusts the idealized target spectra, such as Lorentzian-shaped notches, making them more physically realizable and consistent with the training data distribution. This adjusted spectrum is then passed to the tandem network, which predicts the corresponding structural parameters. The framework effectively handles both narrowband (<50 nm) and highly complex spectra, while addressing the one-to-many mapping challenge inherent in inverse design. The model achieves high accuracy, and the designed spectra closely match full-wave simulation results, validating its practicality for advanced nanophotonic applications.