This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementation philosophies reported in the field. It emphasizes the critical role of cross-disciplinary collaboration in this rapidly evolving field.

The convergence of deep learning and big data has spurred significant interest in developing novel hardware that can run large artificial intelligence (AI) workloads more efficiently. Over the last several years, silicon photonics has emerged as a disruptive technology for next-generation accelerators for machine learning (ML). More recently, the heterogeneous integration of III-V compound semiconductors has opened the door to integrating lasers and semiconductor optical amplifiers at wafer-scale, enabling the scaling of the size, density, and complexity of silicon photonic integrated circuits (PICs). Furthermore, using this technology, all of the individual components required to execute the operations within a neural network are available and can be integrated on the same PIC. Here, we review our innovations of an energy-efficient and scalable silicon photonic platform serving as the underlying foundation for next-generation AI accelerator hardware.

In this work, we describe the hardware implementation of a decision tree (DT) model using our microring-based time-division multiplexing (TDM) optical content-addressable memory (O-CAM) architecture, which uses a customized encoding scheme that allows ternary search. Mainstream machine learning models, such as neural networks and transformers, are capable of impressive accuracy when dealing with unstructured data such as images or text, but DTs still outperform them for regression and classification tasks over tabular data. We developed a novel DT mapping scheme for O-CAM and a new inequality encoding technique, enabling the parallel evaluation of inequalities for all nodes of the DT.

In this work, we discuss our vision for neuromorphic accelerators based on integrated photonics within the framework of the Horizon Europe NEUROPULS project. Augmented integrated photonic architectures that leverage phase-change and III-V materials for optical computing will be presented. A CMOS-compatible platform will be discussed that integrates these materials to fabricate photonic neuromorphic architectures, along with a gem5-based simulation platform to model accelerator operation once it is interfaced with a RISC-V processor. This simulation platform enables accurate system-level accelerator modeling and benchmarking in terms of key metrics such as speed, energy consumption, and footprint.

We report a multi-modal spiking neuron that allows optical and electronic input and control, and wavelength-multiplexing operation, for use in novel high-speed neuromorphic sensing and computing functionalities. The photonic-electronic neuron is built with a micro-scale, nanostructure resonant tunnelling diode (RTD) with photodetection (PD) capability. Leveraging the advantageous intrinsic properties of this RTD-PD system, namely highly nonlinear characteristics, photo-sensitivity, light-induced I-V curve shift, and the ability to deliver excitable responses under electrical and optical inputs, we successfully achieve flexible neuromorphic spike activation and inhibition regimes through photonic-electrical control. We also demonstrate the ability of this RTD-PD spiking sensing-processing neuron to operate under the simultaneous arrival of multiple wavelength-multiplexed optical signals, due to its large photodetection spectral window (covering the 1310 and 1550 nm telecom wavelength bands). Our results highlight the potential of RTD photonic-electronic neurons to reproduce multiple key excitatory and inhibitory spiking regimes, at high speed (ns-rate spiking responses, with faster sub-ns regimes theoretically predicted) and low energy (requiring only ~10 mV and ~150 microW, electrical and optical input amplitudes, respectively), similar in nature to those commonly found in the biological neurons of the visual system and the brain. This work offers a highly promising approach for the realisation of high-speed, energy-efficient photonic-electronic spiking neurons and spiking neural networks, enabling multi-modal and multi-wavelength operation for sensing and information processing tasks. This work therefore paves the way for innovative high-speed, photonic-electronic, and spike-based neuromorphic sensing and computing systems and artificial intelligence hardware.

Spiking neurons and neural networks constitute a fundamental building block for brain-inspired computing, which is poised to benefit significantly from photonic hardware implementations. In this work, we experimentally investigate an interconnected optical neuromorphic system based on an ultrafast spiking vertical cavity surface emitting laser (VCSEL) neuron and a silicon photonics (SiPh) integrated micro-ring resonator (MRR). We experimentally demonstrate two different functional arrangements of these devices: first, we show that MRR weight banks can be used in conjunction with the spiking VCSEL-neurons to perform amplitude weighting of sub-ns optical spiking signals. Second, we show that a continuously firing VCSEL-neuron can be directly modulated using a locking signal propagated through a single weighting MRR, and we utilise this functionality to perform optical spike firing rate-coding via thermal tuning of the MRR. Given the significant track record of both integrated weight banks and photonic VCSEL-neurons, we believe these results demonstrate the viability of combining these two classes of devices for use in functional neuromorphic photonic systems.

Interconnectivity between functional building blocks (such as neurons and synapses) represents a fundamental functionality for realizing neuromorphic systems. However, in the domain of neuromorphic photonics, synaptic interlinking and cascadability of spiking optical artificial neurons remains challenging and mostly unexplored in experiments. In this work, we report an optical synaptic link between optoelectronic spiking artificial neurons based upon resonant tunneling diodes (RTDs) that allows for cascadable spike propagation. First, deterministic spiking is triggered using multimodal (electrical and optical) inputs in RTD-based spiking artificial neurons, which are optoelectronic (OE) circuits incorporating either micron-scale RTDs or photosensitive nanopillar-based RTDs. Second, feedforward linking with dynamical weighting of optical spiking signals between pre- and postsynaptic RTD artificial neurons is demonstrated, including cascaded spike activation. By dynamically weighting the amplitude of optical spikes, it is shown how the cascaded spike activation probability in the postsynaptic RTD node directly follows the amplitude of the weighted optical spikes. This work therefore provides the first experimental demonstration of programmable synaptic optical link and spike cascading between multiple fast and efficient RTD OE spiking artificial neurons, therefore providing a key functionality for photonic-electronic spiking neural networks and light-enabled neuromorphic hardware.

In this work, we present a novel design and implementation of a lens-free phase imaging system with multi-angle illumination that enhances axial resolution and image quality. The technology, which is based on a common-path shearing interferometer with phase shifting, enables ultra-high sensitivity better than 0.2 nm in optical path difference (OPD), while operating over a wide FoV (>10 mm²) and a large volume (>10 mm³). We show results in several applications, from surface topographies to volumetric structures, including imaging of 10 nm thin transparent topographies and of volumetric laser-written refractive index structures in glass. The high sensitivity and low noise make the proposed technology ideally suited for imaging of low contrast structures on the surface or inside transparent materials, such as defects, impurities, or changes in refractive index.

Vertical-Cavity Surface-Emitting Lasers (VCSELs) are highly promising devices for the construction of neuromorphic photonic information processing systems, due to their numerous desirable properties such as low power consumption, high modulation speed, and compactness. Of particular interest is the ability of VCSELs to exhibit neuron-like spiking responses at ultrafast sub-nanosecond rates; thus offering great prospects for high-speed light-enabled spike-based processors. Recent works have shown spiking VCSELs are capable of tackling pattern recognition and image processing problems, but additionally, VCSELs have been used as nonlinear elements in photonic reservoir computing (RC) implementations, yielding state of the art operation. This work introduces and experimentally demonstrates for the first time a new GHz-rate photonic spiking neural network (SNN) built with a single VCSEL neuron. The reported system effectively implements a photonic VCSEL-based spiking reservoir computer, and demonstrates its successful application to a complex nonlinear classification task. Importantly, the proposed system benefits from a highly hardware-friendly, inexpensive realization (a single VCSEL device and off-the-shelf fibre-optic components), for high-speed (GHz-rate inputs) and low-power (sub-mW optical input power) photonic operation. These results open new pathways towards future neuromorphic photonic spike-based processing systems based upon VCSELs (or other laser types) for novel ultrafast machine learning and AI hardware.

Photonic technologies offer great prospects for novel, ultrafast, energy-efficient, and hardware-friendly neuromorphic (brain-like) computing platforms. Moreover, neuromorphic photonic approaches based on ubiquitous, technology-mature, and low-cost vertical-cavity surface-emitting lasers (VCSELs) (devices found in fiber-optic transmitters, mobile phones, and automotive sensors) are of particular interest. Given that VCSELs have shown the ability to realize neuronal optical spiking responses (at ultrafast GHz rates), their use in spike-based information-processing systems has been proposed. In this study, spiking neural network (SNN) operation, based on a hardware-friendly photonic system of just one VCSEL, is reported alongside a novel binary weight “significance” training scheme that fully capitalizes on the discrete nature of the optical spikes used by the SNN to process input information. The VCSEL-based photonic SNN was tested with a highly complex multivariate classification task (MADELON) before its performance was compared using a traditional least-squares training method and an alternative novel binary weighting scheme. Excellent classification accuracies of >94% were achieved by both training methods, exceeding the benchmark performance of the dataset in a fraction of the processing time. The newly reported training scheme also dramatically reduces the training set size requirements and the number of trained nodes (≤1% of the total network node count). This VCSEL-based photonic SNN, in combination with the reported “significance” weighting scheme, therefore grants ultrafast spike-based optical processing highly reduced training requirements and hardware complexity for potential application in future neuromorphic systems and artificial intelligence applications.

Despite remarkable advances in neuromorphic photonics, nanophotonic devices enabling neuron-like spiking emission and detection—needed for spike-based computation—are still at an early stage of development. Here, we present recent achievements on a photonic neuro-architecture based on III–V integrated spiking circuit nodes using nanoscale quantum resonant tunneling diodes (nanoRTDs) with negative differential resistance. We discuss the integration of nanoRTDs with nano-photodetectors (nanoPDs), nano-light-emitting diodes (nanoLEDs) and other light sources (e.g., VCSELs), to realize on-chip sensory neuron emitter and receiver spiking nodes. The RTD-based nanophotonic neuron devices and circuits for sensing and spike processing multi-functionalities pave the way for energy-efficient signal processing and sensory neuromorphic systems in edge computing AI applications.

This work reports a nanostructure resonant tunnelling diode-photodetector (RTD-PD) device and demonstrates its operation as a controllable, optically-triggered excitable spike generator. The top contact layer of the device is designed with a nanopillar structure 500 nm in diameter) to restrain the injection current, yielding therefore lower energy operation for spike generation. We demonstrate experimentally the deterministic optical triggering of controllable and repeatable neuron-like spike patterns in the nanostructure RTD-PDs. Moreover, we show the device's ability to deliver spiking responses when biased in both regions adjacent to the negative differential conductance (NDC) region, the so-called 'peak' and 'valley' points of the current-voltage (I-V) characteristic. This work also demonstrates experimentally key neuron-like dynamical features in the nanostructure RTD-PD, such as a well-defined threshold (in input optical intensity) for spike firing, as well as the presence of spike firing refractory time. The optoelectronic and chip-scale character of the proposed system together with the deterministic, repeatable and well controllable nature of the optically-elicited spiking responses render this nanostructure RTD-PD element as a highly promising solution for high-speed, energy-efficient optoelectronic artificial spiking neurons for novel light-enabled neuromorphic computing hardware.

Optoelectronic artificial spiking neurons are regarded as promising core elements for novel photonic neuromorphic computing hardware. In this work, we investigate a modular optoelectronic spiking neuron built with an excitable resonant tunneling diode (RTD) coupled to a photodetector and a vertical-cavity surface-emitting laser (VCSEL). This work provides the first experimental demonstration of amplitude control of the fired optical spikes in the electrical-to-optical part of the artificial neuron, therefore introducing a simple way of weighting of the presynaptic spikes. This is achieved by tuning the VCSEL bias current, hence providing a straightforward, high-speed, hardware-friendly option for the weighting of optical spiking signals. Furthermore, we validate the feasibility of this layout using a simulation of a monolithically integrated, RTD-based nanoscale optoelectronic spiking neuron model, which confirms the system’s capability to deliver weighted optical spiking signals at GHz firing rates. These results demonstrate a high degree of flexibility of RTD-based artificial optoelectronic spiking neurons and highlight their potential towards compact, high-speed photonic spiking neural networks and light-enabled neuromorphic hardware.

We report a theoretical study on laterally-coupled pairs of vertical-cavity surface-emitting lasers (VCSELs) operated under conditions that generate or suppress high-speed optical spiking regimes, and show their potential in exemplar functionalities for use in photonic neuromorphic computing systems. The VCSEL numerical analysis is based on a system of five coupled mode equations, which, for the case of weak coupling, are reduced to a set of three equations that predict the saddle-node stability boundary in terms of device parameters and operating conditions. These results guide numerical simulation to demonstrate multiple neuron-like dynamics, including single- and multiple-spike emission, spiking inhibition, and rebound spiking directly in the optical domain. Importantly, these behaviours are obtained at sub-nanosecond rates, hence multiple orders of magnitude faster than the millisecond timescales of biological neurons. The mechanisms responsible are explained by reference to appropriate phase portraits. The coupled VCSELs model is then used for demonstration of high-speed, all-optical digital-to-spiking encoding and for representation of digital image data using rate-coded spike trains.

Photonic technologies provide a strong platform for the construction of neuromorphic processing systems, due to the potential for ultrafast, low-power operation on hardware-friendly implementations. One device, the Vertical Cavity Surface Emitting Laser (VCSEL) has demonstrated neural spiking behaviour ideal for such photonic neuromorphic processing [1].

Quantitative phase imaging and holography allow highly sensitive detection of phase changes, for example of surface non-uniformities or refractive index (RI) structures in the volume of a sample. For many applications, wide field-of-view (FoV) and high phase sensitivity are required. Lens-free interferometric microscopy can op-erate over a wide FoV and volume, but with the drawbacks of low axial resolution and poor optical sectioning, which comes with increased noise from out-of-focus planes. In this work, we propose and demonstrate a novel implementation of lens-free phase imaging with phase-shifting interferometry and multi-angle illumination that enhances axial resolution and image quality, enabling ultra-high phase sensitivity with optical path difference (OPD) background root-mean-square (RMS) noise of 0.2 nm, while operating over a wide FoV ( > 10 mm2). As a prototypical application, we demonstrate imaging of 10 nm thin transparent glass structures, and of 3D laser-written structures of RI modifications in glass over a large volume ( > 10 mm3), where the location of features along the z-axis can be determined with 3x higher accuracy compared to an on-axis illumination. The technology is particularly suitable for large-scale analysis and characterization of structures on the surface or embedded in transparent materials.

In this work, we present an optical spike processing architecture based on the combined operation of two types of photonic systems: silicon photonics (SiPh) microring resonator (MRR) weight banks [1] and all-optical spiking artificial neurons based on vertical cavity surface emitting lasers (VCSELs) [2]. These systems have been previously demonstrated as key-enabling building blocks in the rapidly growing field of neuromorphic photonics.

Nanophotonic spiking neural networks (SNNs) based on neuron-like excitable subwavelength (submicrometre) devices are of key importance for realizing brain-inspired, power-efficient artificial intelligence (AI) systems with high degree of parallelism and energy efficiency. Despite significant advances in neuromorphic photonics, compact and efficient nanophotonic elements for spiking signal emission and detection, as required for spike-based computation, remain largely unexplored. In this invited perspective, we outline the main challenges, early achievements, and opportunities toward a key-enabling photonic neuro-architecture using III–V/Si integrated spiking nodes based on nanoscale resonant tunnelling diodes (nanoRTDs) with folded negative differential resistance. We utilize nanoRTDs as nonlinear artificial neurons capable of spiking at high-speeds. We discuss the prospects for monolithic integration of nanoRTDs with nanoscale light-emitting diodes and nanolaser diodes, and nanophotodetectors to realize neuron emitter and receiver spiking nodes, respectively. Such layout would have a small footprint, fast operation, and low power consumption, all key requirements for efficient nano-optoelectronic spiking operation. We discuss how silicon photonics interconnects, integrated photorefractive interconnects, and 3D waveguide polymeric interconnections can be used for interconnecting the emitter-receiver spiking photonic neural nodes. Finally, using numerical simulations of artificial neuron models, we present spike-based spatio-temporal learning methods for applications in relevant AI-based functional tasks, such as image pattern recognition, edge detection, and SNNs for inference and learning. Future developments in neuromorphic spiking photonic nanocircuits, as outlined here, will significantly boost the processing and transmission capabilities of next-generation nanophotonic spike-based neuromorphic architectures for energy-efficient AI applications. This perspective paper is a result of the European Union funded research project ChipAI in the frame of the Horizon 2020 Future and Emerging Technologies Open programme.

We report on Vertical-Cavity Surface-Emitting Lasers (VCSELs) for high-speed and energy-efficiency systems for photonic neuromorphic computing, yielding excellent performance in complex processing tasks whilst benefitting from hardware-friendly implementations and full compatibility with optical communication technologies.

Spiking neurons and neural networks constitute a fundamental building block for brain-inspired computing, which is posed to benefit significantly from photonic hardware implementations. In this work, we experimentally investigate an interconnected system based on an ultrafast spiking VCSEL-neuron and a silicon photonics (SiPh) integrated micro-ring resonator (MRR) weight bank, and demonstrate two different functional arrangements of these devices. First, we show that MRR weightbanks can be used in conjuction with the spiking VCSEL-neurons to perform amplitude weighting of sub-ns optical spiking signals. Second, we show that a continuous firing VCSEL-neuron can be directly modulated using a locking signal propagated through a single weighting micro-ring, and we utilize this functionality to perform optical spike firing rate-coding via thermal tuning of the micro-ring resonator. Given the significant track record of both integrated weight banks and photonic VCSEL-neurons, we believe these results demonstrate the viability of combining these two classes of devices for use in functional neuromorphic photonic systems.