AtOMICS: a deep learning-based automated optomechanical intelligent coupling system for testing and characterization of silicon photonics chiplets - Jaime Gonzalo Flor Flores; Jim Solomon; Connor Nasseraddin; Talha Yerebakan; Andrey B Matsko; Chee Wei Wong (Machine Learning: Science and Technology)
Recent advances in silicon photonics promise to revolutionize modern technology by improving the performance of everyday devices in multiple fields (Thomson et al 2016 J. Opt. 18 073003). However, as the industry moves into a mass fabrication phase, the problem of adequate 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 various types of devices and scenarios. This paper presents 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, to control a testing setup that can process multiple devices and be quickly 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.