Neural Inverse Design of Nanostructures (NIDN).

ABSTRACT: In the recent decade, computational tools have become central in material design, allowing rapid development cycles at reduced costs. Machine learning tools are especially on the rise in photonics. However, the inversion of the Maxwell equations needed for the design is particularly challenging from an optimization standpoint, requiring sophisticated software. We present an innovative, open-source software tool called Neural Inverse Design of Nanostructures (NIDN) that allows designing complex, stacked material nanostructures using a physics-based deep learning approach. Instead of a derivative-free or data-driven optimization or learning method, we perform a gradient-based neural network training where we directly optimize the material and its structure based on its spectral characteristi

SUBMITTER: Gomez P 

PROVIDER: S-EPMC9780235 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature