Spectra2pix: Generating Nanostructure Images from Spectra

TL;DR

Spectra2pix is a deep learning model that generates nanostructure images from spectra, enabling flexible and generalized design of nanostructures for nano-photonics applications.

Contribution

Introduces spectra2pix, a novel DNN architecture capable of generating diverse nanostructure images from spectra, including unseen geometries, enhancing design flexibility.

Findings

Successful generation of nanostructure images from spectra.

Model generalizes to unseen nanostructure geometries.

Enhances applicability in real-world nanostructure design.

Abstract

The design of the nanostructures that are used in the field of nano-photonics has remained complex, very often relying on the intuition and expertise of the designer, ultimately limiting the reach and penetration of this groundbreaking approach. Recently, there has been an increasing number of studies suggesting to apply Machine Learning techniques for the design of nanostructures. Most of these studies engage Deep Learning techniques, which entails training a Deep Neural Network (DNN) to approximate the highly non-linear function of the underlying physical process between spectra and nanostructures. At the end of the training, the DNN allows an on-demand design of nanostructures, i.e. the model can infer nanostructure geometries for desired spectra. In this work, we introduce spectra2pix, which is a model DNN trained to generate 2D images of the designed nanostructures. Our model architecture is not limited to a closed set of nanostructure shapes, and can be trained for the design of any geometry. We show, for the first time, a successful generalization ability by designing a completely unseen sub-family of geometries. This generalization capability highlights the importance of our model architecture, and allows higher applicability for real-world design problems.