  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.
