Excitation transfer between optically injected microdisk lasers

Vaerenbergh, Thomas Van; Alexander, Koen; Dambre, Joni; Bienstman, Peter

doi:10.1364/oe.21.028922

Cited by 30 publications

(17 citation statements)

References 20 publications

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“…In some recent cases, cascadability is used to refer to a single feedforward connection [45,50,82,83], but we argue that this approach does not conclusively demonstrate indefinite physical cascadability. The format of the upstream input can be different than that of the downstream output such that the second might not be able to drive the first.…”

Section: A Claiming Cascadability and Fan-inmentioning

confidence: 87%

Silicon Photonic Modulator Neuron

et al. 2019

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There has been a recently renewed interest in neuromorphic photonics, a field promising to access pivotal and unexplored regimes of machine intelligence. Progress has been made on isolated neurons and analog interconnects; nevertheless, this renewal has yet to produce a demonstration of a silicon photonic neuron capable of interacting with other like neurons. We report a modulator-class photonic neuron fabricated in a conventional silicon photonic process line. We demonstrate behaviors of transfer function configurability, fan-in, inhibition, time-resolved processing, and, crucially, autaptic cascadability -a sufficient set of behaviors for a device to act as a neuron participating in a network of like neurons. The silicon photonic modulator neuron constitutes the final piece needed to make photonic neural networks fully integrated on currently available silicon photonic platforms.In this work, we fabricate and demonstrate a silicon photonic modulator neuron. It consists of a balanced photodetector directly connected to a microring (MRR) modulator. We demonstrate that this device possesses the necessary capabilities of a network-compatible neuron: fan-in, high-gain optical-to-optical nonlinearity, and arXiv:1812.11898v1 [physics.app-ph]

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Section: A Claiming Cascadability and Fan-inmentioning

confidence: 87%

Silicon Photonic Modulator Neuron

et al. 2019

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“…An investigation of photonics for information processing based on spikes has taken place alongside the development of electronic spiking architectures. Since the first demonstration of photonic spike processing by Rosenbluth et al [68], there has been a surge in research related to aspects of spike processing in various photonic devices with a recent bloom of proposed forms of spiking dynamics [57,[69][70][71][72][73][74][75][76][77][78][79][80][81]]-a strategy that could lead to combined computation and communication in the same substrate.…”

Section: Photonic Spike Processingmentioning

confidence: 99%

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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“…Cascadability has been proposed and numerically demonstrated in both optically [44,66] and electrically [39,58,60] injected lasers. Cascadability in optically injected PNNs presents a challenge because optical interference is sensitive to optical phase noise.…”

Section: Cascadabilitymentioning

confidence: 99%

Progress in neuromorphic photonics

et al. 2017

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As society's appetite for information continues to grow, so does our need to process this information with increasing speed and versatility. Many believe that the one-size-fits-all solution of digital electronics is becoming a limiting factor in certain areas such as data links, cognitive radio, and ultrafast control. Analog photonic devices have found relatively simple signal processing niches where electronics can no longer provide sufficient speed and reconfigurability. Recently, the landscape for commercially manufacturable photonic chips has been changing rapidly and now promises to achieve economies of scale previously enjoyed solely by microelectronics. By bridging the mathematical prowess of artificial neural networks to the underlying physics of optoelectronic devices, neuromorphic photonics could breach new domains of information processing demanding significant complexity, low cost, and unmatched speed. In this article, we review the progress in neuromorphic photonics, focusing on photonic integrated devices. The challenges and design rules for optoelectronic instantiation of artificial neurons are presented. The proposed photonic architecture revolves around the processing network node composed of two parts: a nonlinear element and a network interface. We then survey excitable lasers in the recent literature as candidates for the nonlinear node and microring-resonator weight banks as the network interface. Finally, we compare metrics between neuromorphic electronics and neuromorphic photonics and discuss potential applications.

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Excitation transfer between optically injected microdisk lasers

Cited by 30 publications

References 20 publications

Silicon Photonic Modulator Neuron

Silicon Photonic Modulator Neuron

Principles of Neuromorphic Photonics

Progress in neuromorphic photonics

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