Optical injection in semiconductor ring lasers

Coomans, Werner; Beri, Stefano; Sande, Guy Van der; Gelens, Lendert; Danckaert, Jan

doi:10.1103/physreva.81.033802

Cited by 39 publications

(44 citation statements)

References 36 publications

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“…In the unidirectional regime (at I = 2.3 mA), we investigated the effect of optical injection in E + , as a function of the injection amplitude E in = E in, 1 (E in, 2 = 0), and the detuning ∆ω. This configuration for optical injection is identical to the one studied for SRLs in [28]. Consequently, as those SRLs are governed by the same physics, the same locking regimes appear.…”

Section: Optical Injectionmentioning

confidence: 62%

“…Finally, whereas the excitation mechanism proposed in [13,28] is heavily dependent on the precise phase φ c of the intermodal coupling, while φ c is not straightforward to control during fabrication, the currently proposed excitation mechanism is less sensitive to this value, as φ c has less influence on the threshold for optical injection than on the onset of AO.…”

Section: Excitabilitymentioning

confidence: 99%

“…When a subthreshold excitation causes oscillations, as is the case for Hopf bifurcations (Class II excitability, which appears, e.g., in passive silicon microrings [23]) the neuron will act as a resonator: multiple subthreshold excitations can only excite the system if their delay is close to an integer multiple of the natural oscillation period of the oscillations of the system. The excitation mechanism presented by Gelens, Coomans et al [13,15,28] also incorporates resonator instead of integrating behavior. and 5(c) respectively show time traces of input and output pulses for two input pulse rates.…”

Section: Excitabilitymentioning

confidence: 99%

“…In Fig. 2, for simplicity, we omitted the bifurcations corresponding to unstable structures or chaotic regions, as they are already extensively discussed in [28] and not important for the remainder of this paper. In this paper, we focus on the positive unidirectional regime U 1 , and the saddle-node bifurcation SN 1 .…”

Section: Optical Injectionmentioning

confidence: 99%

“…Excitability is typically defined in the context of a small perturbation at the input of a system in a stable equilibrium [28]. When the perturbation is below a certain threshold, the system re- Fig.…”

Section: Excitabilitymentioning

confidence: 99%

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

Alexander¹,

Vaerenbergh²,

Fiers³

et al. 2013

Opt. Express

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Abstract:We demonstrate class I excitability in optically injected microdisk lasers, and propose a possible optical spiking neuron design. The neuron has a clear threshold and an integrating behavior, leading to an output rate-input rate dependency that is comparable to the characteristic of sigmoidal artificial neurons. We also show that the optical phase of the input pulses has influence on the neuron response, and can be used to create inhibitory, as well as excitatory perturbations.

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Section: Optical Injectionmentioning

confidence: 62%

Section: Excitabilitymentioning

confidence: 99%

Section: Excitabilitymentioning

confidence: 99%

Section: Optical Injectionmentioning

confidence: 99%

Section: Excitabilitymentioning

confidence: 99%

See 3 more Smart Citations

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

Alexander¹,

Vaerenbergh²,

Fiers³

et al. 2013

Opt. Express

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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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