Spike propagation in a nanolaser-based optoelectronic neuron

Piwonka, Juan Ortega; Hejda, Matěj; Alanis, Juan Arturo; Lourenço, João; Hurtado, Antonio; Figueiredo, J. M. L.; Romeira, Bruno; Javaloyes, J.

doi:10.1364/ome.451706

Cited by 10 publications

(8 citation statements)

References 42 publications

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“…The PRL model combines an RTD, nanoscale laser and simplified photodetection term coupled via current (with scaling factor κ) to the RTD. The parameters for the I-V characteristic of the RTD [36] and for the laser diode used in this work are representative of nanoscale devices. Although a VCSEL (with a distributed Bragg reflector cavity and multiple-quantum-well active medium) is used in this experiment for the proof-of-concept, this model extrapolates the same functionality towards a nanolaser [37].…”

Section: Simulation Of the Ultrafast Spike Tuneability In The Prl Neuronmentioning

confidence: 99%

“…Additional details on the dynamical model and parameter descriptions are available from previous work [35,36]. The parameters used for the I-V curve following Schulman's model [38]…”

Section: Simulation Of the Ultrafast Spike Tuneability In The Prl Neuronmentioning

confidence: 99%

“…In previous experiments and simulation work, we have investigated some important dynamical behaviors of excitable RTD optoelectronic spiking neurons [22,35] and the operating mechanisms of RTD-based optical links for signal propagation and information processing tasks [36]. Spike amplitude tuning in synaptic links underpins the weighting functionality, which is key to information processing in spiking neural networks (SNNs).…”

Section: Introductionmentioning

confidence: 99%

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Tunable presynaptic weighting in optoelectronic spiking neurons built with laser-coupled resonant tunneling diodes

Zhang

Hejda²,

Малышева³

et al. 2023

J. Phys. D: Appl. Phys.

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Optoelectronic spiking neurons are regarded as highly promising systems for novel light-powered neuromorphic computing hardware. Here, we investigate an optoelectronic (O/E/O) spiking neuron built with an excitable resonant tunnelling diode (RTD) coupled to a photodetector and a vertical-cavity surface-emitting laser (VCSEL). This work provides the first experimental report on the control of the amplitude (weighting factor) of the fired optical spikes directly in the neuron, introducing a simple way for presynaptic spike amplitude tuning. Notably, a very simple mechanism (the control of VCSEL bias) is used to tune the amplitude of the spikes fired by the optoelectronic neuron, hence enabling an easy and high-speed option for the weighting of optical spiking signals in future interconnected photonic spike-processing nodes. Furthermore, we validate the feasibility of this layout using a simulation of a monolithically-integrated, RTD-powered, nanoscale optoelectronic spiking neuron model, confirming the system’s potential for delivering weighted optical spiking signals at very high speeds (GHz firing rates). These results demonstrate the high degree of flexibility of RTD-based artificial optoelectronic spiking neurons and highlight their potential towards compact, high-speed and low-energy photonic spiking neural networks for use in future, light-enabled neuromorphic hardware.

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Section: Simulation Of the Ultrafast Spike Tuneability In The Prl Neuronmentioning

confidence: 99%

“…Additional details on the dynamical model and parameter descriptions are available from previous work [35,36]. The parameters used for the I-V curve following Schulman's model [38]…”

Section: Simulation Of the Ultrafast Spike Tuneability In The Prl Neuronmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tunable presynaptic weighting in optoelectronic spiking neurons built with laser-coupled resonant tunneling diodes

Zhang

Hejda²,

Малышева³

et al. 2023

J. Phys. D: Appl. Phys.

Self Cite

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“…This non-linear behaviour displayed by the RTD is what propels this device to one best candidates for the neuromorphic computing architectures for several reasons. First and foremost, the RTD can exhibit excitable dynamics [3,4] like biological neurons. Excitable dynamics or excitability is the capacity of a system to respond strongly to a weak stimulus if it is over a specific threshold before relaxing down to the same state it was previously to the stimulus [5], if the stimulus does not push the device over the threshold only a small response is produced.…”

Section: Introductionmentioning

confidence: 99%

Resonant Tunnelling Diode – Photodetectors for spiking neural networks

Lourenço

Al-Taai

Al-Khalidi

et al. 2022

J. Phys.: Conf. Ser.

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Spike-based neuromorphic devices promise to alleviate the energy greed of the artificial intelligence hardware by using spiking neural networks (SNNs), which employ neuron like units to process information through the timing of the spikes. These neuron-like devices only consume energy when active. Recent works have shown that resonant tunnelling diodes (RTDs) incorporating optoelectronic functionalities such as photodetection and light emission can play a major role on photonic SNNs. RTDs are devices that display an N-shaped current-voltage characteristics capable of providing negative differential conductance (NDC) over a range of the operating voltages. Specifically, RTD photodetectors (RTD-PDs) show promise due to their unique mixture of the structural simplicity while simultaneously providing highly complex non-linear behavior. The goal of this work is to present a systematic study of the how the thickness of the RTD-PD light absorption layers (100, 250, 500 nm) and the device size impacts on the performance of InGaAs RTD-PDs, namely on its responsivity and time response when operating in the third (1550 nm) optical transmission window. Our focus is on the overall characterization of the device optoelectronic response including the impact of the light absorption on the device static current-voltage characteristic, the responsivity and the photodetection time response. For the static characterization, the devices I-V curves were measured under dark conditions and under illumination, giving insights on the light induced I-V tunability effect. The RTD-PD responsivity was compared to the response of a commercial photodetector. The characterization of the temporal response included its capacity to generate optical induced neuronal-like electrical spike, that is, when working as an opto-to-electrical spike converter. The experimental data obtained at each characterization phase is being used for the evaluation and refinement of a behavioral model for RTD-PD devices under construction.

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“…However, as far as research has progressed, we can frame another substantial reason behind the difference in working principles of ANNs and biological neural networks , i.e., the nonlinear neurons in ANNs are approximators of continuous function, while in contrast, the biological neurons uses binary and temporally precise action potential of asynchronous spikes in order to mark the presence of one event [3]. Thus communication through spikes has proved to be energy efficient [6,7]. Thus, SNNs, comprising of biologically plausible spiking neurons can be considered as a strong candidate for constructing next generation neuromorphic DNNs.…”

Section: Introductionmentioning

confidence: 99%

Realisation of large-scale photonic spiking hardware system

Talukder

Porté

Brunner

2022

Emerging Topics in Artificial Intelligence (ETAI) 2022

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An efficient photonic hardware integration of neural networks can benefit us from the inherent properties of parallelism, high-speed data processing and potentially low energy consumption. In artificial neural networks (ANN), neurons are classified as static, single and continuous-valued. On contrary, information transmission and computation in biological neurons occur through spikes, where spike time and rate play a significant role. Spiking neural networks (SNNs) are thereby more biologically relevant along with additional benefits in terms of hardware friendliness and energy-efficiency. Considering all these advantages, we designed a photonic reservoir computer (RC) based on photonic recurrent spiking neural networks (SNN) i.e. a liquid state machine. It is a scalable proof-of-concept experiment, comprising more than 30,000 neurons. This system presents an excellent testbed for demonstrating next generation bio-inspired learning in photonic systems.

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Spike propagation in a nanolaser-based optoelectronic neuron

Cited by 10 publications

References 42 publications

Tunable presynaptic weighting in optoelectronic spiking neurons built with laser-coupled resonant tunneling diodes

Tunable presynaptic weighting in optoelectronic spiking neurons built with laser-coupled resonant tunneling diodes

Resonant Tunnelling Diode – Photodetectors for spiking neural networks

Realisation of large-scale photonic spiking hardware system

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