Characterization and Optimization of Coherent MZI-Based Nanophotonic Neural Networks Under Fabrication Non-Uniformity

Silicon-photonic neural networks (SPNNs) and artificial intelligence (AI) accelerators have emerged as promising successors to electronic 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 article, we, for the first time, model and explore the impact of optical phase noise due to FPVs in the waveguide width, silicon-on-insulator (SOI) thickness, and etch depth 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 using the optimized MZIs can lead to significant improvements in the network inferencing accuracy. The proposed one-time optimization imposes low area overhead and hence is applicable even to resource-constrained designs.