We report on ultrafast artificial laser neurons and on their potentials for future neuromorphic (brainlike) photonic information processing systems. We introduce our recent and ongoing activities demonstrating controllable excitation of spiking signals in optical neurons based upon vertical-cavity surface emitting lasers (VCSEL-Neurons). These spiking regimes are analogous to those exhibited by biological neurons, but at sub-nanosecond speeds (>7 orders of magnitude faster). We also describe diverse approaches, based on optical or electronic excitation techniques, for the activation/inhibition of sub-ns spiking signals in VCSEL-Neurons. We report our work demonstrating the communication of spiking patterns between VCSEL-Neurons toward future implementations of optical neuromorphic networks. Furthermore, new findings show that VCSEL-Neurons can perform multiple neuro-inspired spike processing tasks. We experimentally demonstrate photonic spiking memory modules using single and mutually coupled VCSEL-Neurons. Additionally, the ultrafast emulation of neuronal circuits in the retina using VCSEL-Neuron systems is demonstrated experimentally for the first time to our knowledge. Our results are obtained with off-the-shelf VCSELs operating at the telecom wavelengths of 1310 and 1550 nm. This makes our approach fully compatible with current optical network and data center technologies, hence offering great potentials for future ultrafast neuromorphic laser-neuron networks for new paradigms in brain-inspired computing and artificial intelligence.

We report experimentally and theoretically on the controllable inhibition of spiking regimes in a 1300 nm wavelength vertical-cavity surface-emitting laser. Reproducible suppression of spiking dynamics is demonstrated at fast operation speeds (up to sub-ns rates) and with total control on the temporal duration of the spiking inhibition windows. This Letter opens new paths toward a photonic inhibitory neuronal model system for use in future neuromorphic photonic information processing modules and which are able to operate at speeds up to 8 orders of magnitude faster than biological neurons.

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.

We report experimentally and in theory on the controllable propagation of spiking regimes between two interlinked vertical-cavity surface-emitting lasers (VCSELs). We show that spiking patterns generated in a first transmitter VCSEL (T-VCSEL) are communicated to a second receiver VCSEL (R-VCSEL), which responds by firing the same spiking response. Importantly, the spiking regimes from both devices had analogous temporal and amplitude characteristics, including equal number of spikes fired, same spike and interspike temporal durations, and similar spike intensity properties. These responses are analogous to the spiking communication patterns of biological neurons yet at subnanosecond speeds, this is several (up to 8) orders of magnitude faster than the timescales of biological neurons. We have also carried out numerical simulations reproducing with high degree of agreement the experimental findings. These results obtained with inexpensive, commercially available VCSELs operating at important telecom wavelengths (1300 nm) offer great prospects for the scaling of emerging VCSEL-based photonic neuronal models into network configurations for use in novel neuromorphic photonic systems. This offers high potentials for nontraditional computing paradigms beyond digital systems and able to operate at ultrafast speeds.

The ever-increasing demand for artificial intelligence (AI) systems is underlining a significant requirement for new, AI-optimised hardware. Neuromorphic (brain-like) processors are one highly-promising solution, with photonic-enabled realizations receiving increasing attention. Among these, approaches based upon vertical cavity surface emitting lasers (VCSELs) are attracting interest given their favourable attributes and mature technology. Here, we demonstrate a hardware-friendly neuromorphic photonic spike processor, using a single VCSEL, for all-optical image edge-feature detection. This exploits the ability of a VCSEL-based photonic neuron to integrate temporally-encoded pixel data at high speed; and fire fast (100 ps-long) optical spikes upon detecting desired image features. Furthermore, the photonic system is combined with a software-implemented spiking neural network yielding a full platform for complex image classification tasks. This work therefore highlights the potential of VCSEL-based platforms for novel, ultrafast, all-optical neuromorphic processors interfacing with current computation and communication systems for use in future light-enabled AI and computer vision functionalities.

All-optical binary convolution with a photonic spiking vertical-cavity surface-emitting laser (VCSEL) neuron is proposed and demonstrated experimentally for the first time, to the best of our knowledge. Optical inputs, extracted from digital images and temporally encoded using rectangular pulses, are injected in the VCSEL neuron, which delivers the convolution result in the number of fast ( < 100 ps long) spikes fired. Experimental and numerical results show that binary convolution is achieved successfully with a single spiking VCSEL neuron and that all-optical binary convolution can be used to calculate image gradient magnitudes to detect edge features and separate vertical and horizontal components in source images. We also show that this all-optical spiking binary convolution system is robust to noise and can operate with high-resolution images. Additionally, the proposed system offers important advantages such as ultrafast speed, high-energy efficiency, and simple hardware implementation, highlighting the potentials of spiking photonic VCSEL neurons for high-speed neuromorphic image processing systems and future photonic spiking convolutional neural networks.