Ultra-compact integrated photonic devices enabled by machine learning and digital metamaterials

Abstract

We demonstrate three ultra-compact integrated-photonics devices, which are designed via a machine-learning algorithm coupled with finite-difference time-domain (FDTD) modeling. By digitizing the design domain into “binary pixels,” these digital metamaterials are readily manufacturable using traditional semiconductor foundry processes. By showing various devices (beam-splitters and waveguide bends), we showcase our approach's generality. With an area footprint smaller than λ 0 2 , our designs are amongst the smallest reported to-date. Our method combines machine learning with digital metamaterials to enable ultra-compact, manufacturable devices, which could power a new “Photonics Moore's Law.”