Letteratura scientifica selezionata sul tema "Excitable microlaser"

Excitability, encountered in numerous fields from biology to neurosciences and optics, is a general phenomenon characterized by an all-or-none response of a system to an external perturbation of a given strength. When subject to delayed feedback, excitable systems can sustain multistable pulsing regimes, which are either regular or irregular time sequences of pulses reappearing every delay time. Here, we investigate an excitable microlaser subject to delayed optical feedback and study the emergence of complex pulsing dynamics, including periodic, quasiperiodic, and irregular pulsing regimes. This work is motivated by experimental observations showing these different types of pulsing dynamics. A suitable mathematical model, written as a system of delay differential equations, is investigated through an in-depth bifurcation analysis. We demonstrate that resonance tongues play a key role in the emergence of complex dynamics, including non-equidistant periodic pulsing solutions and chaotic pulsing. The structure of resonance tongues is shown to depend very sensitively on the pump parameter. Successive saddle transitions of bounding saddle-node bifurcations constitute a merging process that results in unexpectedly large regions of locked dynamics, which subsequently disconnect from the relevant torus bifurcation curve; the existence of such unconnected regions of periodic pulsing is in excellent agreement with experimental observations. As we show, the transition to unconnected resonance regions is due to a general mechanism: the interaction of resonance tongues locally at an extremum of the rotation number on a torus bifurcation curve. We present and illustrate the two generic cases of disconnecting and disappearing resonance tongues. Moreover, we show how a pair of a maximum and a minimum of the rotation number appears naturally when two curves of torus bifurcation undergo a saddle transition (where they connect differently).

Mid‐infrared luminescence at around 1.8 μm has shown great potential in many frontier photonic fields. However, how to realize the 1.8 μm emission of Tm3+ with multiple pump wavelengths and in particular in nanosized hosts has remained a challenge so far. Herein, an erbium sublattice–based core–shell nanostructure is proposed to achieve the multiwavelength excitable mid‐infrared emission of Tm3+ at around 1.8 μm from its 3F4 → 3H6 transition. The core–shell engineering and cross‐relaxation help to improve the population of Er3+ at its 4I13/2 energy level and subsequent energy transfer to Tm3+ (3F4) for its efficient 1.8 μm emission upon 808, 980, and 1530 nm excitations. The modulation of energy‐transfer channels by codoping other rare‐earth ions shows that introducing a small amount of Ce3+ into the erbium sublattice can enhance the 1.8 μm emission of Tm3+ through favorable cross‐relaxation processes. Moreover, the 1.8 μm emission is further significantly enhanced by designing a core–shell–shell nanostructure with a NaYF4:Yb‐sensitizing interlayer, which is able to maximize the absorption of 980 nm excitation energy. These results provide a new conceptual nanosized model for mid‐infrared luminescent materials toward infrared biophotonics and microlasers.

Cette thèse présente des recherches sur des systèmes de calcul alternatifs, en se concentrant spécifiquement sur le calcul analogique et neuromimétique. La quête d'une intelligence artificielle plus générale a mis en évidence les limitations des unités de calcul conventionnelles basées sur les architectures de Von Neumann, en particulier en termes d'efficacité énergétique et de complexité. Les architectures de calcul inspirées du cerveau et les ordinateurs analogiques sont des prétendants de premier plan dans ce domaine. Parmi les différentes possibilités, les systèmes photoniques impulsionnels (spiking) offrent des avantages significatifs en termes de vitesse de traitement, ainsi qu'une efficacité énergétique accrue. Nous proposons une approche novatrice pour les tâches de classification et de reconnaissance d'images en utilisant un laser à micropilier développé en interne fonctionnant comme un neurone artificiel. La non-linéarité du laser excitable, résultant des dynamiques internes, permet de projeter les informations entrantes, injectées optiquement dans le micropilier au travers de son gain, dans des dimensions supérieures. Cela permet de trouver des régions linéairement séparables pour la classification. Le micropilier laser excitable présente toutes les propriétés fondamentales d'un neurone biologique, y compris l'excitabilité, la période réfractaire et l'effet de sommation, avec des échelles caractéristiques de fonctionnement sous la nanoseconde. Cela en fait un candidat de premier choix dans les systèmes impulsionnels où la dynamique de l'impulsion elle-même porte des informations, par opposition aux systèmes qui considèrent uniquement la fréquence moyenne des impulsions. Nous avons conçu et étudié plusieurs systèmes utilisant le micropilier laser, basés sur un calculateur à réservoir à nœud physique unique qui émule un calculateur à plusieurs noeuds et utilisant différents régimes dynamiques du microlaser. Ces systèmes ont atteint des performances de reconnaissance plus élevées par rapport aux systèmes sans le microlaser. De plus, nous introduisons un nouveau modèle inspiré des champs réceptifs dans le cortex visuel, capable de classifier un ensemble de chiffres tout en éliminant le besoin d'un ordinateur conventionnel dans le processus. Ce système a été mis en œuvre expérimentalement avec succès en utilisant une configuration optique combinée en espace libre et fibrée, ouvrant des perspectives intéressantes pour le calcul analogue ultra-rapide sur architecture matérielle
This thesis presents research on alternative computing systems, with a focus on analog and neuromimetic computing. The pursuit of more general artificial intelligence has underscored limitations in conventional computing units based on Von Neumann architectures, particularly regarding energy efficiency and complexity. Brain-inspired computing architectures and analog computers are key contenders in this field. Among the various proposed methods, photonic spiking systems offer significant advantages in processing and communication speeds, as well as potential energy efficiency. We propose a novel approach to classification and image recognition tasks using an in-house developed micropillar laser as the artificial neuron. The nonlinearity of the spiking micropillar laser, resulting from the internal dynamics of the system, allows for mapping incoming information, optically injected to the micropillar through gain, into higher dimensions. This enables finding linearly separable regions for classification. The micropillar laser exhibits all fundamental properties of a biological neuron, including excitability, refractory period, and summation effect, with sub-nanosecond characteristic timescales. This makes it a strong candidate in spiking systems where the dynamics of the spike itself carries information, as opposed to systems that consider spiking rates only. We designed and studied several systems using the micropillar laser, based on a reservoir computer with a single physical node that emulates a reservoir computer with several nodes, using different dynamical regimes of the microlaser. These systems achieved higher performance in prediction accuracy of the classes compared to systems without the micropillar. Additionally, we introduce a novel system inspired by receptive fields in the visual cortex, capable of classifying a digit dataset entirely online, eliminating the need for a conventional computer in the process. This system was successfully implemented experimentally using a combined fiber and free-space optical setup, opening promising prospects for ultra-fast, hardware based feature selection and classification systems