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Thermal Crosstalk Modelling and Compensation Methods for Programmable Photonic Integrated Circuits
Here, we train and experimentally evaluate three models incorporating varying degrees of physics intuition to predict the effect of thermal crosstalk in different locations of an integrated programmable photonic mesh.
Addressing Data Scarcity in Optical Matrix Multiplier Modeling Using Transfer Learning
We present and experimentally evaluate using transfer learning to address experimental data scarcity when training neural network (NN) models for Mach-Zehnder interferometer mesh-based optical matrix multipliers.
Data-efficient Modeling of Optical Matrix Multipliers Using Transfer Learning
We demonstrate transfer learning-assisted neural network models for optical matrix multipliers with scarce measurement data.
Data-driven Modeling of Mach-Zehnder Interferometer-based Optical Matrix Multipliers
Photonic integrated circuits are facilitating the development of optical neural networks, which have the potential to be both faster and more energy efficient than their electronic counterparts since optical signals are especially well-suited for implementing matrix multiplications.
Comparison of Models for Training Optical Matrix Multipliers in Neuromorphic PICs
We experimentally compare simple physics-based vs. data-driven neural-network-based models for offline training of programmable photonic chips using Mach-Zehnder interferometer meshes.
