AtOMICS: A neural network-based Automated Optomechanical Intelligent Coupling System for testing and characterization of silicon photonics chiplets

TL;DR

This paper introduces AtOMICS, a neural network-based automated system for testing and characterizing silicon photonics chiplets, enhancing efficiency and generalizability in the mass production of photonic devices.

Contribution

The paper presents a novel neural network system that automates testing and active alignment of silicon photonic devices, integrating computer vision and time-series analysis for versatile and stable operation.

Findings

System operates at vacuum and atmospheric conditions

Maintains stability over more than a month

Capable of processing multiple device types

Abstract

Recent advances in silicon photonics promise to revolutionize modern technology by improving performance of everyday devices in multiple fields. However, as the industry moves into a mass fabrication phase, the problem of effective testing of integrated silicon photonics devices remains to be solved. A cost-efficient manner that reduces schedule risk needs to involve automated testing of multiple devices that share common characteristics such as input-output coupling mechanisms, but at the same time needs to be generalizable to multiple types of devices and scenarios. In this paper we present a neural network-based automated system designed for in-plane fiber-chip-fiber testing, characterization, and active alignment of silicon photonic devices that use process-design-kit library edge couplers. The presented approach combines state-of-the-art computer vision techniques with time-series analysis, in order to control a testing setup that can process multiple devices and can be easily tuned to incorporate additional hardware. The system can operate at vacuum or atmospheric pressures and maintains stability for fairly long time periods in excess of a month.