arXiv subjects

Xavier Porte

Publications and source records attributed to Xavier Porte.

At least 19 recordsLinked to original sources

arXiv2026

Chip-scale nanostructured chaotic billiards for broadband speckle spectrometry

Computational on-chip spectrometers are emerging as a powerful platform for portable spectral analysis, combining photonic integration with advanced signal processing to enable a wide range of in-situ sensing applications. We propose a broadband reconstructive spectrometer based on wave chaos in a stadium microresonator with a nanostructured scattering layer for full-area speckle readout. Wavelength dependent interference within the chaotic microresonator encodes the spectral information into a spatial intensity pattern that can be computationally inverted to reconstruct the input spectra. The optimal fabrication parameters of the SU-8 polymer nanostructured layer yield a surface roughness of 176nm and a root mean square thickness of ~2um. We experimentally validate our spectrometer at visible and infrared wavelengths, with resolutions of 43pm at 630nm and 8.2pm at 1550nm. The spectral reconstruction is demonstrated for single and multiple narrowline sources as well as for a broadband (~1nm) pulsed laser source. The broad experimental validation and compact footprint (0.05mm2) establishes our chaotic microresonator-based speckle spectrometer as a robust and versatile platform for high-resolution, on-chip spectral sensing.

physics.optics↗

arXiv2026

Neuron Surface Emitting Laser (NeuronSEL): Spiking Regimes and Negative Differential Resistance in Solitary Multi-junction VCSELs

Neuromorphic photonics is emerging as a powerful platform for fast and efficient optical information processing and sensing. However, future brain-inspired photonic systems require compact and scalable light sources, capable of generating the neuro-mimetic optical signals needed for their operation. This work demonstrates a single-stack laser that delivers optical and electrical neural-like spiking emission under solitary operation. Termed the Neuron Surface-Emitting Laser (NeuronSEL), this compact, multi-junction Vertical-Cavity Surface Emitting Laser (VCSEL) exhibits non-linear Negative Differential Resistance (NDR), similar to that observed in memristive devices. Leveraging this NDR behaviour enables the novel demonstration of multiple neuronal features in the NeuronSEL including refractoriness and threshold-/integrate-and-fire dynamics. We demonstrate the NeuronSEL's behaviour as an optical spiking neuron and its ability to perform processing functions, such as coincidence detection and exclusive OR operations. Its scalability is illustrated by proposing a network based on an array of NeuronSELs, able to perform classification tasks. The NeuronSEL emerges as a strong candidate for practical and scalable neuromorphic photonic hardware, with potential impact across a range of applications in optical sensing, communications and computing technologies, whilst benefitting from the inherent advantages of VCSEL technology -low manufacturing cost, compactness, efficiency, vertical emission, and straightforward integration into large arrayed-structures and networks.

physics.optics↗

arXiv2026

Single Pixel Image Classification using an Ultrafast Digital Light Projector

Pattern recognition and image classification are essential tasks in machine vision. Autonomous vehicles, for example, require being able to collect the complex information contained in a changing environment and classify it in real time. Here, we experimentally demonstrate image classification at multi-kHz frame rates combining the technique of single pixel imaging (SPI) with a low complexity machine learning model. The use of a microLED-on-CMOS digital light projector for SPI enables ultrafast pattern generation for sub-ms image encoding. We investigate the classification accuracy of our experimental system against the broadly accepted benchmarking task of the MNIST digits classification. We compare the classification performance of two machine learning models: An extreme learning machine (ELM) and a backpropagation trained deep neural network. The complexity of both models is kept low so the overhead added to the inference time is comparable to the image generation time. Crucially, our single pixel image classification approach is based on a spatiotemporal transformation of the information, entirely bypassing the need for image reconstruction. By exploring the performance of our SPI based ELM as binary classifier we demonstrate its potential for efficient anomaly detection in ultrafast imaging scenarios.

cs.CV↗

arXiv2025

On-chip wave chaos for photonic extreme learning

The increase in demand for scalable and energy efficient artificial neural networks has put the focus on novel hardware solutions. Integrated photonics offers a compact, parallel and ultra-fast information processing platform, specially suited for extreme learning machine (ELM) architectures. Here we experimentally demonstrate a chip-scale photonic ELM based on wave chaos interference in a stadium microcavity. By encoding the input information in the wavelength of an external single-frequency tunable laser source, we leverage the high sensitivity to wavelength of injection in such photonic resonators. We fabricate the microcavity with direct laser writing of SU-8 polymer on glass. A scattering wall surrounding the stadium operates as readout layer, collecting the light associated with the cavity's leaky modes. We report uncorrelated and aperiodic behavior in the speckles of the scattering barrier from a high resolution scan of the input wavelength. Finally, we characterize the system's performance at classification in four qualitatively different benchmark tasks. As we can control the number of output nodes of our ELM by measuring different parts of the scattering barrier, we demonstrate the capability to optimize our photonic ELM's readout size to the performance required for each task.

physics.optics↗

arXiv2025

Spiking Rate and Latency Encoding with Resonant Tunnelling Diode Neuron Circuits and Design Influences

Neuromorphic computing, inspired by the functionality and efficiency of biological neural systems, holds promise for advancing artificial intelligence and computational paradigms. Resonant tunneling diodes (RTDs), thanks to their ability to generate neuronal dynamical responses, such as excitable spiking and refractoriness, have recently emerged as candidates for use as opto-electronic spiking neurons in novel neuromorphic computing hardware. This work explores the ability of RTD spiking neurons to deliver information encoding mechanisms analogous to those observed in biological neurons, specifically spike firing rate and spike latency encoding. We also report detailed experimental and numerical studies on the impact that the RTD mesa radius and circuit inductance and capacitance have on its spiking properties, providing useful information for future design of RTD-based neural networks optimised for ultrafast (>GHz rate) processing capabilities. Finally, we showcase the application of spike rate encoding in RTD neurons for the ultrafast reconstruction of a eight-level amplitude signal, effectively filtering out the noise.

physics.app-ph↗

arXiv2025

Roadmap on Neuromorphic Photonics

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.

cs.ET↗

arXiv2024

A spiking photonic neural network of 40.000 neurons, trained with rank-order coding for leveraging sparsity

Spiking neural networks are neuromorphic systems that emulate certain aspects of biological neurons, offering potential advantages in energy efficiency and speed by for example leveraging sparsity. While CMOS-based electronic SNN hardware has shown promise, scalability and parallelism challenges remain. Photonics provides a promising platform for SNNs due to the speed of excitable photonic devices standing in as neurons and the parallelism and low-latency of optical signal conduction. Here, we present a photonic SNN comprising 40,000 neurons using off-the-shelf components, including a spatial light modulator and a CMOS camera, enabling scalable and cost-effective implementations for photonic SNN proof of concept studies. The system is governed by a modified Ikeda map, were adding additional inhibitory feedback forcing introduces excitability akin to biological dynamics. Using latency encoding and sparsity, the network achieves 83.5% accuracy on MNIST using 22% of neurons, and 77.5% with 8.5% neuron utilization. Training is performed via liquid state machine concepts combined with the hardware-compatible SPSA algorithm, marking its first use in photonic neural networks. This demonstration integrates photonic nonlinearity, excitability, and sparse computation, paving the way for efficient large-scale photonic neuromorphic systems.

cs.ET↗

arXiv2023

Convergence and scaling of Boolean-weight optimization for hardware reservoirs

Hardware implementation of neural network are an essential step to implement next generation efficient and powerful artificial intelligence solutions. Besides the realization of a parallel, efficient and scalable hardware architecture, the optimization of the system's extremely large parameter space with sampling-efficient approaches is essential. Here, we analytically derive the scaling laws for highly efficient Coordinate Descent applied to optimizing the readout layer of a random recurrently connection neural network, a reservoir. We demonstrate that the convergence is exponential and scales linear with the network's number of neurons. Our results perfectly reproduce the convergence and scaling of a large-scale photonic reservoir implemented in a proof-of-concept experiment. Our work therefore provides a solid foundation for such optimization in hardware networks, and identifies future directions that are promising for optimizing convergence speed during learning leveraging measures of a neural network's amplitude statistics and the weight update rule.

stat.ML↗

arXiv2023

Deterministic and stochastic coarsening control in optically-addressed spatial light modulators subject to optical feedback

Phase separation accompanied by further domain growth and coarsening is a phenomenon common to a broad variety of dynamical systems. In this connection, controlling such processes represents a relevant interdisciplinary problem. Using methods of numerical modelling, we demonstrate two approaches for the coarsening control in bistable systems based on the example of a spatially-extended model describing an optically-addressed spatial light modulator with two color illumination subject to optical feedback. The first method implies varying system parameters such that the system evolves as the pitchfork or saddle-node normal forms. The second method leverages noise whose intensity is used as an additional system parameter. Both, deterministic and stochastic schemes allow to control the direction and speed of the fronts separating spatial domains. The considered stochastic control represents a particular case of the noise-sustained front propagation in bistable systems and involves the properties of the optical system under study. In contrast, the proposed deterministic control technique can be applied to bistable systems of different nature.

physics.optics↗

arXiv2023

Additive 3D photonic integration that is CMOS compatible

Today, continued miniaturization in electronic integrated circuits (ICs) appears to have reached its fundamental limit. At the same time, energy consumption due by communication becomes the dominant limitation in high performance electronic ICs for computing, and modern computing concepts such a neural networks further amplify the challenge. Photonic communication is a promising strategy to address the second, while adding a third dimension to the predominantly two dimensional integrated circuits appears the most promising future strategy for further IC architecture improvement. Crucial for efficient electronic-photonic co-integration is CMOS compatibility. Here, we review our latest results obtained in the FEMTO-ST RENATECH facilities on using additive photo-induced polymerization of a standard photo-resin for truly 3D photonic integration according to these principles. Based on one- and two-photon polymerization and combined with direct-laser writing, we 3D-printed air- and polymer-cladded photonic waveguides. An important application of such circuits are the interconnects of optical neural networks, where 3D integration enables scalability in terms of network size versus its geometric dimensions. In particular via \emph{flash}-TPP, a fabrication process combining blanket one- and high-resolution two-photon polymerization, we demonstrated polymer-cladded step-index waveguides with up to 6~mm length, low insertion ($\sim$0.26~dB) and propagation ($\sim$1.3~dB/mm) losses, realized broadband and low loss ($\sim$0.06~dB splitting losses) adiabatic 1 to M couplers as well as tightly confining air-cladded waveguides for denser integration. By stably printing such integrated photonic circuits on standard semiconductor samples, we show the concept's CMOS compatibility. With this, we lay out a promising, future avenue for scalable integration of hybrid photonic and electronic components.

physics.optics↗

arXiv2022

(3+1)D-printed adiabatic 1-to-N broadband couplers

We report single-mode 3D optical couplers leveraging adiabatic power transfer towards up to 4 output ports. We use the CMOS compatible additive (3+1)D \emph{flash}-TPP printing for fast and scalable fabrication. Coupling optical losses of such devices are reduced below $\sim$~0.06~dB by tailoring the coupling and waveguides geometry, and we demonstrate almost octave-spanning broadband functionality from 520~nm to 980~nm.

physics.optics↗

arXiv2021

Computational metrics and parameters of an injection-locked large area semiconductor laser for neural network computing

Artificial neural networks have become a staple computing technique in many fields. Yet, they present fundamental differences with classical computing hardware in the way they process information. Photonic implementations of neural network architectures potentially offer fundamental advantages over their electronic counterparts in terms of speed, processing parallelism, scalability and energy efficiency. Scalable and high performance photonic neural networks (PNNs) have been demonstrated, yet they remain scarce. In this work, we study the performance of such a scalable, fully parallel and autonomous PNN based on a large area vertical-cavity surface-emitting laser (LA-VCSEL). We show how the performance varies with different physical parameters, namely, injection wavelength, injection power, and bias current. Furthermore, we link these physical parameters to the general computational measures of consistency and dimensionality. We present a general method of gauging dimensionality in high dimensional nonlinear systems subject to noise, which could be applied to many systems in the context of neuromorphic computing. Our work will inform future implementations of spatially multiplexed VCSEL PNNs.

cs.ET↗

arXiv2021

Photonic neuromorphic computing using vertical cavity semiconductor lasers

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.

cs.ET↗

arXiv2021

Combining one and two photon polymerization for accelerated high performance (3+1)D photonic integration

Dense and efficient circuits with component sizes approaching the physical limit is the hallmark of high performance integration. However, current fabrication technology is mostly constraint to 2D lithography, and thermal energy dissipation induced by switching electronic signal lines presents a fundamental challenge for truly 3D electronic integration. Photonics reduces this problem, and direct laser writing of a photoresin is a promising high-resolution tool for 3D photonic integration. Here, we combine one and two-photon polymerization (TPP) for waveguide integration for the first time, dramatically accelerating the fabrication process and increasing optical confinement. 3D additive printing is based on femtosecond TPP, while blanket irradiation with a UV lamp induces one-photon polymerization (OPP) throughout the entire 3D chip. We locally and dynamically adjust writing conditions to implement (3+1)D \emph{flash}-TPP: waveguide cores are printed with a small distance between neighboring writing voxels to ensure smooth interfaces, mechanical support structures are printed at maximal distance between the voxels to speed up the process. Finally, the entire chip's \emph{passive} volume not part of waveguide cores or mechanical support is polymerized in a single instance by UV blanket irradiation. This decouples fabrication time from the passive volume's size. We succeed in printing vertical single-mode waveguides of 6~mm length that reach up to NA = 0.16. Noteworthy, we achieve exceptionally low -0.26 dB injection and very low propagation losses of -1.36 dB/mm, which is within one order of magnitude of standard integrated silicon photonics. Finally, the optical performance of our waveguides does not deteriorate during 120 days within our measurement uncertainty.

physics.optics↗

arXiv2021

Two-color optically-addressed spatial light modulator as generic spatio-temporal systems

Nonlinear spatio-temporal systems are the basis for countless physical phenomena in such diverse fields as ecology, optics, electronics and neuroscience. The canonical approach to unify models originating from different fields is the normal form description, which determines the generic dynamical aspects and different bifurcation scenarios. Realizing different types of dynamical systems via one experimental platform that enables continuous transition between normal forms through tuning accessible system parameters is therefore highly relevant. Here, we show that a transmissive, optically-addressed spatial light modulator under coherent optical illumination and optical feedback coupling allows tuning between pitchfork, transcritical and saddle-node bifurcations of steady states. We demonstrate this by analytically deriving the system's normal form equations and confirm these results via extensive numerical simulations. Our model describes a nematic liquid crystal device using nano-dimensional dichalcogenide (a-As$_2$S$_3$) glassy thin-films as photo sensors and alignment layers, and we use device parameters obtained from experimental characterization. Optical coupling, for example using diffraction, holography or integrated unitary maps allow implementing a variety of system topologies of technological relevance for neural networks and potentially XY-Hamiltonian models with ultra low energy consumption.

physics.optics↗

arXiv2021

Direct (3+1)D laser writing of graded-index optical elements

We propose single-step additive fabrication of graded-index optical elements by introducing the light exposure as the additional dimension to three-dimensional (3D) laser writing, hence (3+1)D writing. We use a commercial printer and photoresist to realize the proposed single step fabrication method that can be swiftly adopted for research and engineering. After presenting the characterization of the graded-index profiles via basic structures, we demonstrate two different optical devices: volume holograms that are superimposed using angular and peristrophic multiplexing, and optical waveguides with well-defined refractive index profiles. In the latter, we precisely control the propagating modes via tuning the (3+1)D-printed waveguide parameters and report step-index and graded-index core-cladding transitions.

physics.optics↗

arXiv2020

A complete, parallel and autonomous photonic neural network in a semiconductor multimode laser

Neural networks are one of the disruptive computing concepts of our time. However, they fundamentally differ from classical, algorithmic computing in a number of fundamental aspects. These differences result in equally fundamental, severe and relevant challenges for neural network computing using current computing substrates. Neural networks urge for parallelism across the entire processor and for a co-location of memory and arithmetic, i.e. beyond von Neumann architectures. Parallelism in particular made photonics a highly promising platform, yet until now scalable and integratable concepts are scarce. Here, we demonstrate for the first time how a fully parallel and fully implemented photonic neural network can be realized using spatially distributed modes of an efficient and fast semiconductor laser. Importantly, all neural network connections are realized in hardware, and our processor produces results without pre- or post-processing. 130+ nodes are implemented in a large-area vertical cavity surface emitting laser, input and output weights are realized via the complex transmission matrix of a multimode fiber and a digital micro-mirror array, respectively. We train the readout weights to perform 2-bit header recognition, a 2-bit XOR and 2-bit digital analog conversion, and obtain < 0.9 10^-3 and 2.9 10^-2 error rates for digit recognition and XOR, respectively. Finally, the digital analog conversion can be realized with a standard deviation of only 5.4 10^-2. Our system is scalable to much larger sizes and to bandwidths in excess of 20 GHz.

cs.NE↗

arXiv2020

Boolean learning under noise-perturbations in hardware neural networks

A high efficiency hardware integration of neural networks benefits from realizing nonlinearity, network connectivity and learning fully in a physical substrate. Multiple systems have recently implemented some or all of these operations, yet the focus was placed on addressing technological challenges. Fundamental questions regarding learning in hardware neural networks remain largely unexplored. Noise in particular is unavoidable in such architectures, and here we investigate its interaction with a learning algorithm using an opto-electronic recurrent neural network. We find that noise strongly modifies the system's path during convergence, and surprisingly fully decorrelates the final readout weight matrices. This highlights the importance of understanding architecture, noise and learning algorithm as interacting players, and therefore identifies the need for mathematical tools for noisy, analogue system optimization.

cs.NE↗