# Designing nanophotonic structures using conditional-deep convolutional generative adversarial networks

arXiv: https://arxiv.org/abs/1903.08432 · HTML: https://mlsanity.com/paper/1903.08432/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2019.

## Built on by (most cited first, 16 of 16)

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- [Reinforcement learning framework for the mechanical design of microelectronic components under multiphysics constraints](https://mlsanity.com/paper/2504.17142/) (2025)
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- [Two-Stage Deformable-Convolutional Inverse Design of Nanophotonic Absorbers from Optical Spectra](https://mlsanity.com/paper/2608.11860/) (2026)

## Abstract (arXiv)

Data-driven design approaches based on deep-learning have been introduced in nanophotonics to reduce time-consuming iterative simulations which have been a major challenge. Here, we report the first use of conditional deep convolutional generative adversarial networks to design nanophotonic antennae that are not constrained to a predefined shape. For given input reflection spectra, the network generates desirable designs in the form of images; this form allows suggestions of new structures that cannot be represented by structural parameters. Simulation results obtained from the generated designs agreed well with the input reflection spectrum. This method opens new avenues towards the development of nanophotonics by providing a fast and convenient approach to design complex nanophotonic structures that have desired optical properties.