  Silicon-photonic neural networks (SPNNs) offer substantial improvements in
computing speed and energy efficiency compared to their digital electronic
counterparts. However, the energy efficiency and accuracy of SPNNs are highly
impacted by uncertainties that arise from fabrication-process and thermal
variations. In this paper, we present the first comprehensive and hierarchical
study on the impact of random uncertainties on the classification accuracy of a
Mach-Zehnder Interferometer (MZI)-based SPNN. We show that such impact can vary
based on both the location and characteristics (e.g., tuned phase angles) of a
non-ideal silicon-photonic device. Simulation results show that in an SPNN with
two hidden layers and 1374 tunable-thermal-phase shifters, random uncertainties
even in mature fabrication processes can lead to a catastrophic 70% accuracy
loss.
