  Silicon-photonic neural networks (SPNNs) have emerged as promising successors
to electronic artificial intelligence (AI) accelerators by offering orders of
magnitude lower latency and higher energy efficiency. Nevertheless, the
underlying silicon photonic devices in SPNNs are sensitive to inevitable
fabrication-process variations (FPVs) stemming from optical lithography
imperfections. Consequently, the inferencing accuracy in an SPNN can be highly
impacted by FPVs -- e.g., can drop to below 10% -- the impact of which is yet
to be fully studied. In this paper, we, for the first time, model and explore
the impact of FPVs in the waveguide width and silicon-on-insulator (SOI)
thickness in coherent SPNNs that use Mach-Zehnder Interferometers (MZIs).
Leveraging such models, we propose a novel variation-aware, design-time
optimization solution to improve MZI tolerance to different FPVs in SPNNs.
Simulation results for two example SPNNs of different scales under realistic
and correlated FPVs indicate that the optimized MZIs can improve the
inferencing accuracy by up to 93.95% for the MNIST handwritten digit dataset --
considered as an example in this paper -- which corresponds to a <0.5% accuracy
loss compared to the variation-free case. The proposed one-time optimization
method imposes low area overhead, and hence is applicable even to
resource-constrained designs
