Spike-based information encoding in vertical cavity surface emitting lasers for neuromorphic photonic systems

Hejda, Matěj; Robertson, Joshua; Bueno, Julián; Hurtado, Antonio

doi:10.1088/2515-7647/aba670

Cited by 31 publications

(29 citation statements)

References 64 publications

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“…Josephson junctions [10], micropillar lasers [24], excitable semiconductor lasers, including a graphene laser with saturable absorber [25], quantum-dot laser [26][27][28], microring resonators [29], vertical cavity surface emitting lasers (VCSELs) [30][31][32] and multi-section VCSELs with saturable absorber [33,34]. Table I (PDC) [36] as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Resonant Tunneling Diode Nano-Optoelectronic Excitable Nodes for Neuromorphic Spike-Based Information Processing

et al. 2022

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tivity and component density, with dozens of transistors 43 required per neuron and additional external memories 44 needed for synaptic weights. This results in several µm 45 large neurons. Since dedicated wiring for every synap-46 tic link is not practical, neuromorphic electronic systems 47 usually employ a shared digital communication bus with 48 time-division multiplexing [5], gaining interconnectivity 49 at the expense of bandwidth, or use schemes such as 50 address-event representation (AER) [6]. As an alterna-51 tive, hardware technologies relying on physics for neuro-52 morphic computation are nowadays gaining increasing re-53 search interest. These include hybrid CMOS/memristive 54 systems (see [2] for an overview), spintronics [7] and pho-55 tonic systems [8, 9]. 56 57 Neuromorphic photonics is a nascent field, recently 58 gaining significant traction due to increasing importance 59 of AI algorithms and rapid advances in the field of pho-60 tonic integrated circuits (PICs). Optoelectronic systems 61 in particular are considered as highly suitable for future 62 cognitive computing hardware, as they benefit from op-63 eration with both electrons and photons, each excelling 64 at different key functionalities [14]. Thanks to their ca-65 pability to address bandwidth and interconnect energy 66 limits in a scalable fashion, optoelectronic systems might 67 prove as the optimal solution to overcome these limita-68 tions [15]. There are many different approaches to re-69 alization of artificial neural networks in optics (see for 70 example review [16]). Using delayed feedback, recurrent 71 neural networks can be realized in a photonic reservoir 72 computer, yielding networks with large number of virtual 130 and operation of RTDs with delayed feedback [42], ad-131 dressing only operation of a single (solitary) device. In 132 this work, we investigate interconnected systems con-133 sisting of multiple independent RTD-based monolithic 134 integrated optoelectronic nodes. We employ the nodes 135 as stateless excitable devices and take advantage of the 136 spike-based signalling to implement information process-137 ing tasks and multi-device networks with prospects for 138 very low footprint, low energy and high-speed operation 139 due to the use of sub-λ elements. We utilize two types 140 of nodes: an electronic-optical (E/O) RTD-LD system, 141 realized with a RTD element coupled to a nanoscale laser 142 diode (LD), and an optical-electronic (O/E) RTD-PD 143 system, realized with a photodiode (PD) coupled to a 144 RTD element. In both node types, spiking threshold can 145 be adjusted via bias voltage tuning. An illustration of 146 two nodes with an unidirectional optical weighted link, 147 representing two feedfoward linked neurons, is depicted 148 in Fig. 1a. 149 A. Optoelectronic RTD-system architecture 150 In both the RTD-LD and RTD-PD nodes, the two 151 functional blocks are integrated in a monolithic, metal 152 dielectric cavity micro-pillar with DBQW regions on 153 GaAs/AlGaAs materials [43] for operation at ...

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Section: Introductionmentioning

confidence: 99%

Resonant Tunneling Diode Nano-Optoelectronic Excitable Nodes for Neuromorphic Spike-Based Information Processing

et al. 2022

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“…Without any specific control, giant pulses are expected to pop up randomly and may not be reliable for applications. Yet, extensive studies about controllable giant pulses can be found in the literature [113][114][115][116] with the view of taking advantage of these sudden bursts for high precision remote-sensing [117] or photonic reservoir computing [118].…”

Section: Giant Pulses In Opticsmentioning

confidence: 99%

A review of recent results of mid-infrared quantum cascade photonic devices operating under external optical control

Spitz¹,

Grillot²

2022

J. Phys. Photonics

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The purpose of this article is to gather recent findings about the non-linear dynamics of distributed feedback quantum cascade lasers, with a view on practical applications in a near future. As opposed to other semiconductor lasers, usually emitting in the visible or the near-infrared region, quantum cascade laser technology takes advantage of intersubband transitions and quantum engineering to emit in the mid-infrared and far-infrared domain. This peculiarity and its physical consequences were long considered as a detrimental characteristic to generate non-linear dynamics under external optical control. However, we show that a wide diversity of phenomena, from high-dimensional chaos to giant pulses can be observed when the quantum cascade laser is under external optical feedback or under optical injection and with a continuous current bias. Most of these phenomena have already been observed in other semiconductor lasers under optical feedback or under optical injection, which allows us comparing quantum cascade lasers with their interband counterparts.

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“…high speed, ease to integrate in 2D arrays, low energy operation, high coupling efficiency to optical fibres, etc.). A number of neuronal functionalities have been recently demonstrated in VCSELs, including ultrafast spike activation and inhibition 32 , networked spiking communication 33 , spike rate encoding 34 and pattern recognition 35 , to name a few. Furthermore, theoretical spin flip model (SFM) studies 36 indicate that VCSELs have the potential to present a pathway to unsupervised learning, via the neuron-inspired spike-timing dependent plasticity (STDP) technique.…”

Section: Introductionmentioning

confidence: 99%

Ultrafast neuromorphic photonic image processing with a VCSEL neuron

Robertson

Kirkland

Alanis

et al. 2022

Sci Rep

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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.

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Spike-based information encoding in vertical cavity surface emitting lasers for neuromorphic photonic systems

Cited by 31 publications

References 64 publications

Resonant Tunneling Diode Nano-Optoelectronic Excitable Nodes for Neuromorphic Spike-Based Information Processing

Resonant Tunneling Diode Nano-Optoelectronic Excitable Nodes for Neuromorphic Spike-Based Information Processing

A review of recent results of mid-infrared quantum cascade photonic devices operating under external optical control

Ultrafast neuromorphic photonic image processing with a VCSEL neuron

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