# Comparison of Models for Training Optical Matrix Multipliers in Neuromorphic PICs

arXiv: https://arxiv.org/abs/2111.14787 · HTML: https://mlsanity.com/paper/2111.14787/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 10 references that are in the map.
- First posted on arXiv 23 Nov 2021.

## Builds on (most cited first, 4 of 4)

- [Photonics for artificial intelligence and neuromorphic computing](https://mlsanity.com/paper/2011.00111/) (2020)
- [Neuromorphic Silicon Photonic Networks](https://mlsanity.com/paper/1611.02272/) (2016)
- [Reprogrammable Electro-Optic Nonlinear Activation Functions for Optical Neural Networks](https://mlsanity.com/paper/1903.04579/) (2019)
- [Design of optical neural networks with component imprecisions](https://mlsanity.com/paper/2001.01681/) (2019)

## Built on by (most cited first, 1 of 1)

- [Data-driven Modeling of Mach-Zehnder Interferometer-based Optical Matrix Multipliers](https://mlsanity.com/paper/2210.09171/) (2022)

## Abstract (arXiv)

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. The neural-network model outperforms physics-based models for a chip with thermal crosstalk, yielding increased testing accuracy.