Photonic neuromorphic computing using vertical cavity semiconductor lasers

TL;DR

This paper reviews the use of vertical-cavity surface-emitting lasers (VCSELs) in photonic neuromorphic computing, highlighting their energy efficiency, ultra-fast modulation, and potential for scalable integrated neural networks.

Contribution

It provides a comprehensive overview of VCSEL-based photonic neural networks, emphasizing their compatibility with CMOS, nonlinear response, and integration potential for scalable neuromorphic hardware.

Findings

VCSELs achieve 30% wall-plug efficiency and >30 GHz bandwidth.

VCSELs are highly nonlinear, suitable for all-optical and electro-optical neurons.

Potential for 3D photonic integration with VCSEL arrays.

Abstract

Photonic realizations of neural network computing hardware are a promising approach to enable future scalability of neuromorphic computing. In this review we provide an overview on vertical-cavity surface-emitting lasers (VCSELs) and how these high-performance electro-optical components either implement or are combined with additional photonic hardware to demonstrate points (i-iii). In the neurmorphic photonics' context, VCSELs are of exceptional interest as they are compatible with CMOS fabrication, readily achieve 30\% wall-plug efficiency and >30~GHz modulation bandwidth and hence are highly energy efficient and ultra-fast. Crucially, they react highly nonlinear to optical injection as well as to electrical modulation, making them highly suitable as all-optical as well as electro-optical photonic neurons. Their optical cavities are wavelength-limited, and standard semiconductor growth and lithography enables non-classical cavity configurations and geometries. This enables excitable VCSELs (i.e. spiking VCSELs) to finely control their temporal and spatial coherence, to unlock Terahertz bandwidths through spin-flip effects, and even to leverage cavity quantum electrodynamics to further boost their efficiency. Finally, as VCSEL arrays they are compatible with standard 2D photonic integration, but their emission vertical to the substrate makes them ideally suited for scalable integrated networks leveraging 3D photonic waveguides. Here, we discuss the implementation of spatially as well as temporally multiplexed VCSEL neural networks and reservoirs, computation on the basis of excitable VCSELs as photonic spiking neurons, as well as concepts and advances in the fabrication of VCSELs and microlasers.