Excitability in optically injected microdisk lasers with phase controlled excitatory and inhibitory response

Alexander, Koen; Vaerenbergh, Thomas Van; Fiers, Martin; Mechet, Pauline; Dambre, Joni; Bienstman, Peter

doi:10.1364/oe.21.026182

Cited by 62 publications

(29 citation statements)

References 31 publications

(79 reference statements)

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“…Both microdisk lasers are assumed to be identical, unless mentioned otherwise. Both lasers are locked in the unidirectional regime, close to the saddle-node bifurcation along which excitability was demonstrated in [14]. All splitters in the geometry have a 50/50 splitting ratio.…”

Section: Introductionmentioning

confidence: 69%

“…If the first disk gets excited, its output pulse will travel through the connecting waveguide to the second disk and also this pulse's power will get attenuated by a factor of four. To increase the possibility that the output from the first disk excites the second one, the detuning of the locking signal is slightly different in this paper compared to [14] (∆ω = ω in − ω disk = −20 ns −1 instead of −15 ns −1 ). The current is still 2.3 mA.…”

Section: Introductionmentioning

confidence: 96%

“…The modes are evanescently coupled to a silicon waveguide in the SiO 2 -substrate layer. The evolution of the slowly varying complex field amplitudes of the modes can be described by the rate equations discussed in [13,14]. For relatively large currents, microdisk lasers tend to lase unidirectionally, and input pulses can switch the lasing direction [13].…”

Section: Introductionmentioning

confidence: 99%

“…One way of doing this, as demonstrated in equivalently behaving Semiconductor Ring Lasers (SRLs) [3], is by inducing asymmetry in the intermodal coupling. However, we have recently proposed to use optical injection for this goal, creating a dominating mode (E + ) and a suppressed mode (E − ) [14]. Similar to optically injected single-mode semiconductor lasers [4,5,15], this gives rise, in the vicinity of a Saddle-Node on an Invariant Circle (SNIC) bifurcation, to class I excitability, which phenomenologically resembles the well-known leaky integrate-and-fire model of a spiking neuron [16].…”

Section: Introductionmentioning

confidence: 99%

“…Using the same 'neuron' topology and model parameters proposed in our previous paper [14], which exploits the class I excitability mechanism of a microdisk, and by connecting two of these neurons in the topology defined in section 2, we will demonstrate in section 3 cascadability of the excitation mechanism, as this topology allows one disk to trigger an excitation in the second disk. In sections 4 and 5, the sensitivity of this cascade of two disks to variations in laser frequencies and current deviations, respectively, will be discussed.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Excitation transfer between optically injected microdisk lasers

Vaerenbergh¹,

Alexander²,

Dambre³

et al. 2013

Opt. Express

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Abstract:Recently, we have theoretically demonstrated that optically injected microdisk lasers can be tuned in a class I excitable regime, where they are sensitive to both inhibitory and excitatory external input pulses. In this paper, we propose, using simulations, a topology that allows the disks to react on excitations from other disks. Phase tuning of the intermediate connections allows to control the disk response. Additionally, we investigate the sensitivity of the disk circuit to deviations in driving current and locking signal wavelength detuning. Using state-of-the-art fabrication techniques for microdisk laser, the standard deviation of the lasing wavelength is still about one order of magnitude too large. Therefore, compensation techniques, such as wavelength tuning by heating, are necessary.

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Section: Introductionmentioning

confidence: 69%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Excitation transfer between optically injected microdisk lasers

Vaerenbergh¹,

Alexander²,

Dambre³

et al. 2013

Opt. Express

Self Cite

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Photonic Neuromorphic Pattern Recognition with a Spiking DFB‐SA Laser Subject to Incoherent Optical Injection

Zhang,

Xiang,

et al. 2024

Laser & Photonics Reviews

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Photonic neuromorphic computing is a competitive paradigm to overcome the bottleneck of von Neumann architectures. Incoherent and coherent synaptic networks are two popular schemes realizing photonic weighting functions. Previous works have proved the distributed feedback (DFB) laser with an intracavity saturable absorber (DFB‐SA) can behavior like a spiking neuron. However, the compatibility with the incoherent synaptic architecture has not yet been demonstrated. Here the neuron‐like dynamics of a DFB‐SA laser subject to single‐wavelength and multiple‐wavelengths incoherent optical injections are experimentally demonstrated. The results show that, for the DFB‐SA laser subject to single‐wavelength incoherent injection, the neuron‐like dynamics including threshold, temporal integration, and refractory period are achieved. Besides, the range of injection wavelength that leads to a successful neuron‐like response is identified. For the DFB‐SA laser with four‐wavelength incoherent optical injection, the neuron‐like dynamics can also be achieved. In addition, the effect of wavelength interval is also considered. The logic XOR operation and Iris recognition tasks are successfully implemented. Furthermore, the feasibility of a cascaded system for the DFB‐SA lasers with four‐wavelengths incoherent optical injection is demonstrated. This work provides a feasible scheme for the system integration of photonic spiking neurons and incoherent synaptic networks.

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Principles of Neuromorphic Photonics

Shastri¹,

Tait²,

Lima³

et al. 2018

Unconventional Computing

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GLOSSARYBenchmark A standardized task that can be performed by disparate computing approaches, used to assess their relative processing merit in specific cases. Bifurcation A qualitative change in behavior of a dynamical system in response to parameter variation. Examples include cusp (from monostable to bistable), Hopf (from stable to oscillating), transcritical (exchange of stability between two steady states). Brain-inspired computing (a.k.a. neuro-inspired computing) A biologically inspired approach to build processors, devices, and computing models for applications including adaptive control, machine learning, and cognitive radio. Similarities with biological signal processing include architectural, such as distributed, representational, such as analog or spiking, or algorithmic, such as adaptation. Broadcast and Weight A multi-wavelength analog networking protocol in which multiple all photonic neuron outputs are multiplexed and distributed to all neuron inputs. Weights are reconfigured by tunable spectral filters. Excitability A far-from-equilibrium nonlinear dynamical mechanism underlying all-or-none responses to small perturbations. Fan-in The number of inputs to a neuron. Layered network A network topology consisting of a series of sets (i.e., layers) of neurons. The neurons in each set project their outputs only to neurons in the subsequent layer. Most commonly used type of network used for machine learning. Metric A quantity assessing performance of a device in reference to a specific computing approach. Microring weight bank A silicon photonic implementation of a reconfigurable spectral filter capable of independently setting transmission at multiple carrier wavelengths. Modulation The act of representing an abstract variable in a physical quantity, such as photon rate (i.e., optical power), free carrier density (i.e., optical gain), carrier drift (i.e., current). Electrooptic modulators are devices that convert from an electrical signal to the power envelope of an optical signal. Recently, the scientific community has set out to build bridges between the domains of photonic device physics and neural networks, giving rise to the field of neuromorphic photonics (Fig. 1). This article reviews the recent progress in integrated neuromorphic photonics. We provide an overview of neuromorphic computing, discuss the associated technology (microelectronic and photonic) platforms and compare their metric performance. We discuss photonic neural network approaches and challenges for integrated neuromorphic photonic processors while providing an in-depth description of photonic neurons and a candidate interconnection architecture. We conclude with a future outlook of neuro-inspired photonic processing.tion processing, describe the photonic neural-network approaches being developed by our lab and others, and photonic integrated circuit (PIC) platforms, which have recently undergone rapid growth. PICs are becoming a

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Excitability in optically injected microdisk lasers with phase controlled excitatory and inhibitory response

Cited by 62 publications

References 31 publications

Excitation transfer between optically injected microdisk lasers

Excitation transfer between optically injected microdisk lasers

Photonic Neuromorphic Pattern Recognition with a Spiking DFB‐SA Laser Subject to Incoherent Optical Injection

Principles of Neuromorphic Photonics

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