Photonic neuromorphic computing using vertical cavity semiconductor lasers

Abstract

Photonic realizations of neural network computing hardware are a promising approach to enable future scalability of neuromorphic computing. The number of special purpose neuromorphic hardware and neuromorphic photonics has accelerated on such a scale that one can now speak of a Cambrian explosion. Work along these lines includes (i) high performance hardware for artificial neurons, (ii) the efficient and scalable implementation of a neural network’s connections, and (iii) strategies to adjust network connections during the learning phase. 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, >30 GHz modulation bandwidth and multiply and accumulate operations at sub-fJ energy. They hence are highly energy efficient and ultra-fast. Crucially, they react nonlinearly 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. Finally, we provide an outlook and a roadmap identifying future possibilities and some crucial milestones for the field.