# Data-driven Modeling of Mach-Zehnder Interferometer-based Optical Matrix Multipliers

arXiv: https://arxiv.org/abs/2210.09171 · HTML: https://mlsanity.com/paper/2210.09171/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 40 references that are in the map.
- First posted on arXiv 17 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Deep physical neural networks enabled by a backpropagation algorithm for arbitrary physical systems](https://mlsanity.com/paper/2104.13386/) (2021)
- [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)
- [Silicon microring synapses enable photonic deep learning beyond 9-bit precision](https://mlsanity.com/paper/2104.01164/) (2021)
- [Noise mitigation strategies in physical feedforward neural networks](https://mlsanity.com/paper/2204.09461/) (2022)
- [Comparison of Models for Training Optical Matrix Multipliers in Neuromorphic PICs](https://mlsanity.com/paper/2111.14787/) (2021)

## Built on by (most cited first, 3 of 3)

- [Impact of white noise in artificial neural networks trained for classification: performance and noise mitigation strategies](https://mlsanity.com/paper/2411.04354/) (2024)
- [Thermal Crosstalk Modelling and Compensation Methods for Programmable Photonic Integrated Circuits](https://mlsanity.com/paper/2404.10589/) (2024)
- [Impact of white Gaussian internal noise on analog echo-state neural networks](https://mlsanity.com/paper/2405.07670/) (2024)

## Abstract (arXiv)

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. However, accurate programming of photonic chips for optical matrix multiplication remains a difficult challenge. Here, we describe both simple analytical models and data-driven models for offline training of optical matrix multipliers. We train and evaluate the models using experimental data obtained from a fabricated chip featuring a Mach-Zehnder interferometer mesh implementing 3-by-3 matrix multiplication. The neural network-based models outperform the simple physics-based models in terms of prediction error. Furthermore, the neural network models are also able to predict the spectral variations in the matrix weights for up to 100 frequency channels covering the C-band. The use of neural network models for programming the chip for optical matrix multiplication yields increased performance on multiple machine learning tasks.