Shaping freeform nanophotonic devices with geometric neural parameterization

TL;DR

Neuroshaper introduces a neural network-based geometric parameterization framework for freeform nanophotonic device design, enabling effective optimization, constraint enforcement, and topology manipulation in complex geometries.

Contribution

The paper presents Neuroshaper, a novel neural network-based method for shape optimization of nanophotonic devices that captures complex geometries and enforces constraints effectively.

Findings

Neuroshaper can optimize diverse nanophotonic devices.

It effectively enforces design and manufacturing constraints.

Experimental results validate its practical applicability.

Abstract

Nanophotonic freeform design has the potential to push the performance of optical components to new limits, but there remains a challenge to effectively perform optimization while reliably enforcing design and manufacturing constraints. We present Neuroshaper, a framework for freeform geometric parameterization in which nanophotonic device layouts are defined using an analytic neural network representation. Neuroshaper serves as a qualitatively new way to perform shape optimization by capturing multi-scalar, freeform geometries in an overparameterized representation scheme, enabling effective optimization in a smoothened, high dimensional geometric design space. We show that Neuroshaper can enforce constraints and topology manipulation in a manner where local constraints lead to global changes in device morphology. We further show numerically and experimentally that Neuroshaper can apply to a diversity of nanophotonic devices. The versatility and capabilities of Neuroshaper reflect the ability of neural representation to augment concepts in topological design.