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Addressing optical modulator non-linearities for photonic neural networks.

Peter Seigo Kincaid¹, Nicola Andriolli², Giampiero Contestabile¹

¹Scuola Superiore Sant'Anna, Via G. Moruzzi 1, Pisa, 56124, Italy.

Communications Engineering

|March 27, 2025

Summary

Analog photonics offers high-speed neuromorphic computing but faces noise and distortion. This study analyzes and minimizes non-linearities in modulators, finding Mach-Zehnder interferometers suitable for specific machine learning architectures.

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Area of Science:

Neuromorphic Computing
Analog Photonics
Integrated Photonics

Background:

Analog photonic systems offer high computational speeds and reduced power consumption for neuromorphic computing.
Non-linear distortions and noise in analog systems limit signal resolution and overall functionality.

Purpose of the Study:

To develop a method for analyzing and minimizing non-linearities in optical power transfer functions of modulators.
To compare different modulator types (Mach-Zehnder interferometer, micro-ring modulator, ring-assisted Mach-Zehnder interferometer) and their suitability for analog photonic processors.
To evaluate three analog photonic processor architectures for machine learning applications based on multiplexing techniques.

Main Methods:

Analysis of non-linearities in the optical power transfer function of generic modulators.
Comparative performance evaluation of Mach-Zehnder interferometer, micro-ring modulator, and ring-assisted Mach-Zehnder interferometer.
Application of the analysis to wavelength, space, and time division multiplexing architectures for machine learning.

Main Results:

A method for analyzing and minimizing modulator non-linearities was presented and applied.
Mach-Zehnder interferometers, despite lower maximum resolution, demonstrated superior balance in stability and power consumption for specific architectures.
The study identified optimal design and operation conditions for analog photonic processors.

Conclusions:

Mach-Zehnder interferometers are a balanced choice for space and time division multiplexing analog photonic processor architectures in machine learning.
Minimizing non-linearities is crucial for enhancing the performance and reliability of analog photonic systems.
The presented analysis method aids in the informed design of next-generation neuromorphic computing hardware.