Inverse design of photonic devices with strict foundry fabrication constraints

TL;DR

This paper presents a novel inverse design method for nanophotonic devices that ensures strict adherence to fabrication constraints, leveraging machine learning techniques to optimize designs efficiently and reliably.

Contribution

The authors introduce a machine learning-based inverse design approach that guarantees compliance with foundry-specific length scale constraints in photonic device fabrication.

Findings

Successfully designed photonic components with strict fabrication constraints

Demonstrated reliable and efficient optimization process

Validated designs meet commercial foundry standards

Abstract

We introduce a new method for inverse design of nanophotonic devices which guarantees that resulting designs satisfy strict length scale constraints - including minimum width and spacing constraints required by commercial semiconductor foundries. The method adopts several concepts from machine learning to transform the problem of topology optimization with strict length scale constraints to an unconstrained stochastic gradient optimization problem. Specifically, we introduce a conditional generator for feasible designs and adopt a straight-through estimator for backpropagation of gradients to a latent design. We demonstrate the performance and reliability of our method by designing several common integrated photonic components.