Inverse Design of Photonic Devices with Strict Foundry Fabrication Constraints

We introduce a new method for the inverse design of nanophotonic devices that guarantees that the 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 the backpropagation of gradients to a latent design. We demonstrate the performance and reliability of our method by designing several common integrated photonic components.