AI-Designed Photonics Gratings with Experimental Verification

TL;DR

This paper presents an AI-based method using transformer models to automatically design photonics gratings with verified experimental performance, enabling precise optical addressing in ion traps.

Contribution

It introduces a novel AI software that automates the design of photonics gratings with integrated FDTD simulation for performance verification, achieving high accuracy in optical targeting.

Findings

AI-designed gratings shoot light within 2 microns of target

FDTD simulation FWHM deviations are less than 2 microns

Successfully fabricated gratings for ion trap optical addressing

Abstract

Artificial Intelligence (AI) software based on transformer model is developed to automatically design gratings for possible integrations in ion traps to perform optical addressing on ions. From the user-defined (x,z) coordinates and full-width half-maximum (FWHM) values, the AI software can automatically generate the Graphic Design System (GDS) layout of the grating that shoots light towards the pre-defined (x,z) coordinates with built-in finite-difference time-domain (FDTD) simulation for performance verification. Based on the FDTD verification, AI-design gratings produced grating-to-free-space light that shoots towards the provided (x,z) target with < 2 micron deviations. For most attempts, the FWHM of FDTD simulation has < 2 micron deviations from the user-defined FWHM. The AI-designed gratings were successfully taped out and capable of producing output light for possible optical addressing of trapped ions.