Leaky Integrate‐and‐Fire Mechanism in Exciton–Polariton Condensates for Photonic Spiking Neurons

Abstract

This paper introduces a new approach to neuromorphic photonics in which microcavities exhibiting strong exciton–photon interaction may serve as building blocks of optical spiking neurons. The experimental results demonstrate the intrinsic property of exciton–polaritons to resemble the Leaky Integrate‐and‐Fire (LIF) spiking mechanism. It is shown that exciton–polariton microcavities when non‐resonantly pumped with a pulsed laser exhibit leaky integration due to relaxation of the excitonic reservoir, threshold‐and‐fire mechanism due to transition to Bose–Einstein Condensate (BEC), and resetting due to stimulated emission of photons. These effects, evidenced in photoluminescence characteristics, arise within sub‐ns timescales. The presented approach provides means for ultrafast processing of spike‐like laser pulses with energy efficiency at the level below 1 pJ per spike.

All‐Optical Data Processing with Photon‐Avalanching Nanocrystalline Photonic Synapse

“…Although a very simple, singular operational PAS unit has been studied here, further developments toward designing more complex, hybrid, and parallel optical processors seem feasible, as light confinement and guiding by optical fibers, waveguides, [59] nanoresonators, [13,26,60] metamaterials, [61] or plasmonic nanoantenna [62,63] materials has been intensively studied and may enable input data delivery, reprogramming, input/output data transfer, and storage. A concern related to PAS could be the stimulation source, but robust, cheap, and fast, electronically controlled single mode semiconductor lasers in the NIR spectral region exist (e.g., at 1064 or 852 nm).…”

“…[11,12] Their behavior may additionally promise augmented understanding of nervous systems by real-time, on-site neural activity monitoring and interpretation, or provide feedback in a closed loop. [10] Such artificial synapses were based on electrically stimulated phasechange materials, [9] layered semiconductor microcavities, [13] ferroelectric materials, [14] field-effect transistors, [15,16] metalinsulator-metal heterostructures, [17] carbon nanotubes, [18] layered black phosphorus, [19] NbOx/TaOx memristors, [20] WOx, [12] organic conducting polymers, [21,22] and many others [2,20] including photonic devices too. [8] However, luminescent materials were rarely evaluated in this context [23,24] despite some clear and expected advantages of optical stimulation.…”

Memlumor: A Luminescent Memory Device for Energy-Efficient Photonic Neuromorphic Computing

“…Figure illustrates that the response to the Write burst with N = 40 consists of 40 overlapping PL decays with a progressive increase in their initial PL amplitude after each excitation pulse with a slight decay between the pulses. This process is a consequence of a single-pulse change in the state vector X ⃗ of the sample, demonstrating the so-called potentiation behavior identified for neurons and electrical and optical memory devices. − ,− …”

“…As an example, the photonics community has adopted the memristive paradigm for the conception of electro-optical and all-optically driven memories. − These efforts are motivated by the advantages and new opportunities for the devices operating in the presence of light, such as massive frequency-multiplexing and spatial parallelism supported by generous nonlinear dynamics present in optical systems. − Importantly, optical-based memristors can operate at the single-photon level, opening the route toward room-temperature photonic quantum memristors . Despite most of the photonic memristors relying on hybrid electro-optical signal input, , thus inheriting some of the disadvantages of the classical electrically driven memristors, some reports demonstrated neuron-like features in all-optical memory devices. − …”

Analog Photonics Computing for Information Processing, Inference, and Optimization

“…[82] The RC scheme on a silicon photonic chip with optical waveguides, splitters and optical combiners can achieve the data processing rate of 0.12 and up to 12.5 Gbit s -1 . [268] Moreover, more exotic physical systems, such as exciton-polaritons, can reach similar performance so that the SNN architectures can achieve the characteristic operation time of the order of 100 ps with the energy efficiency of 1 pJ per spike [261,262] Optic-based spin machines also enjoy competitive speed characteristics. CIM evolved from having just 4 spins and 12 connections in 2014 (Stanford) to 16K spins and 256M connections in 2021.…”