Addressing data scarcity in optical matrix multiplier modeling using transfer learning

Abstract

We present and experimentally evaluate the use of transfer learning to address experimental data scarcity when training neural network (NN) models for Mach–Zehnder interferometer mesh-based optical matrix multipliers. Our approach involves pretraining the model using synthetic data generated from a less accurate analytical model and fine-tuning it with experimental data. Our investigation demonstrates that this method yields significant reductions in modeling errors compared to using an analytical model or a standalone NN model when training data is limited. Utilizing regularization techniques and ensemble averaging, we achieve <1 dB root-mean-square error on the 3×3 matrix weights implemented by a photonic chip while using only 25% of the available data.