Inverse design of photonic devices with strict foundry fabrication constraints

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.

arXiv comment16 pages, 17 figures

References in corpus (11)

• Outlook for inverse design in nanophotonics
• Quantum circuits with many photons on a programmable nanophotonic chip
• Adjoint method and inverse design for nonlinear nanophotonic devices
• Neural Nano-Optics for High-quality Thin Lens Imaging
• Inverse-designed photonic circuits for fully passive, bias-free Kerr-based nonreciprocal transmission and routing
• Inverse design of photonic crystals through automatic differentiation
• On-chip integrated laser-driven particle accelerator
• Topology optimization of freeform large-area metasurfaces
• Forward-Mode Differentiation of Maxwell's Equations
• Leveraging Continuous Material Averaging for Inverse Electromagnetic Design
• Inverse Design of Compact Multimode Cavity Couplers