All-Optical Nonlinear Activations Using Saturable Absorbers on a Generic InP Platform for Photonic Neural Networks

We experimentally demonstrate all-optical nonlinear activation functions using standalone saturable absorbers, as well as saturable absorbers integrated into microring resonators and Mach-Zehnder interferometer structures on a monolithic InP platform. These configurations enable programmable nonlinear responses and support both passive and active operation modes. The measured activation characteristics exhibit softplus- and ReLU-like behaviors, suitable for neuromorphic computing. When incorporated into a convolutional neural network for MNIST digit classification, the system achieves over 97% accuracy, highlighting the potential of SA-based photonic components for compact, low-power, and scalable optical neural networks.

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Published
2025-11-04
DOI
https://doi.org/10.23919/piers-fall62445.2025.11394274
Primary Topic
Neural Networks and Reservoir Computing
Type
article
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article

All-Optical Nonlinear Activations Using Saturable Absorbers on a Generic InP Platform for Photonic Neural Networks

Rajib Ghosh, Lukas Puts, Julian König, Weiming Yao
Neural Networks and Reservoir Computing
article

All-Optical Nonlinear Activations Using Saturable Absorbers on a Generic InP Platform for Photonic Neural Networks

Rajib Ghosh, Lukas Puts, Julian König, Weiming Yao
article en

Abstract

We experimentally demonstrate all-optical nonlinear activation functions using standalone saturable absorbers, as well as saturable absorbers integrated into microring resonators and Mach-Zehnder interferometer structures on a monolithic InP platform. These configurations enable programmable nonlinear responses and support both passive and active operation modes. The measured activation characteristics exhibit softplus- and ReLU-like behaviors, suitable for neuromorphic computing. When incorporated into a convolutional neural network for MNIST digit classification, the system achieves over 97% accuracy, highlighting the potential of SA-based photonic components for compact, low-power, and scalable optical neural networks.

Eindhoven University of Technology (NL)
Affordable and clean energy
Openalex Percentile: Top 14%
Neural Networks and Reservoir Computing
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