# Noise Analysis of Photonic Modulator Neurons

## TL;DR

This paper analyzes noise propagation in photonic neuron circuits, highlighting how modulator nonlinearities can suppress noise and discussing tradeoffs between power consumption and signal quality in different transimpedance configurations.

## Contribution

It provides a detailed examination of noise sources in modulator-based photonic neurons and evaluates passive versus active transimpedance approaches for noise suppression and power efficiency.

## Key findings

- Modulator nonlinear transfer functions can suppress noise propagation.
- Passive transimpedance circuits are sufficient with efficient modulators.
- Active transimpedance amplifiers can relax power-noise tradeoffs.

## Abstract

Neuromorphic photonics relies on efficiently emulating analog neural networks at high speeds. Prior work showed that transducing signals from the optical to the electrical domain and back with transimpedance gain was an efficient approach to implementing analog photonic neurons and scalable networks. Here, we examine modulator-based photonic neuron circuits with passive and active transimpedance gains, with special attention to the sources of noise propagation. We find that a modulator nonlinear transfer function can suppress noise, which is necessary to avoid noise propagation in hardware neural networks. In addition, while efficient modulators can reduce power for an individual neuron, signal-to-noise ratios must be traded off with power consumption at a system level. Active transimpedance amplifiers may help relax this tradeoff for conventional p-n junction silicon photonic modulators, but a passive transimpedance circuit is sufficient when very efficient modulators (i.e. low C and low V-pi) are employed.