# ITO-based Electro-absorption Modulator for Photonic Neural Activation Function

## TL;DR

This paper presents an ITO-based electro-absorption modulator functioning as a nonlinear activation in photonic neural networks, enabling efficient optical computation for machine learning with a novel integrated approach.

## Contribution

It introduces a new electro-absorption modulator using ITO for nonlinear activation in photonic neural circuits, demonstrating its effectiveness in a 200-node MNIST classification task.

## Key findings

- Successful implementation of an ITO-based electro-absorption modulator as a nonlinear activation.
- Achieved comparable performance to electronic modules in a 200-node MNIST neural network.
- Demonstrated potential for integrated photonic neural networks with efficient nonlinear activation.

## Abstract

Recently integrated optics has become an intriguing platform for implementing machine learning algorithms and inparticular neural networks. Integrated photonic circuits can straightforwardly perform vector-matrix multiplicationswith high efficiency and low power consumption by using weighting mechanism through linear optics. Although,this can not be said for the activation function which requires either nonlinear optics or an electro-optic module withan appropriate dynamic range. Even though all-optical nonlinear optics is potentially faster, its current integrationis challenging and is rather inefficient. Here we demonstrate an electro-absorption modulator based on an IndiumTin Oxide layer, whose dynamic range is used as nonlinear activation function of a photonic neuron. The nonlinearactivation mechanism is based on a photodiode, which integrates the weighed products, and whose photovoltage drivesthe elecro-absorption modulator. The synapse and neuron circuit is then constructed to execute a 200-node MNISTclassification neural network used for benchmarking the nonlinear activation function and compared with an equivalentelectronic module.