A micrometer-size movable light emitting area in a resonant tunneling light emitting diode

Pettinari, G.; Balakrishnan, Nilanthy; Makarovsky, O.; Campion, R. P.; Polimeni, A.; Capizzi, M.; Patanè, A.

doi:10.1063/1.4844975

Cited by 3 publications

(4 citation statements)

References 22 publications

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“…This minimizes the impact of the QRT on the LED optical performance, as previously considered in the monolithic integration of large area RTDlasers [55,56]. This is different from the situation where the QRT is embedded in the active region which shows poor performance at RT mainly attributed to the inefficient resonant tunneling hole injection and other hole current contributions [52,53]. The QRT enables control of the injection of electrons into the active region of the LED.…”

Section: Neuromorphic Nanoled Devicementioning

confidence: 99%

“…Remarkably, the theoretical cut-off frequency of single DBQW-QRTs is limited by the tunneling escape time in the quantum well [44]. As a result, the quantum resonant tunneling effect and NDC characteristic has been exploited in a wide-range of electronic and photonic devices, including THz quantum cascade lasers for gas sensing [46], THz emitters and detectors for imaging [47], and communications beyond 5G (world record oscillation at 1.92 THz [45]), single-photon switches and detectors [48,49], electroluminescence in III-nitride LED sources [50], III-V unipolar bistable QRT [51], bipolar QRT-based LEDs [52][53][54] and lasers [55,56], near-IR photodetectors for optical communications [57], and mid-IR detectors for sensing [58], to name only a few. For neuron computation, early works evoked QRTbased devices as potential nanoelectronic candidates for cellular neural networks as a form of threshold logical gates [59].…”

Section: Introductionmentioning

confidence: 99%

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NanoLEDs for energy-efficient and gigahertz-speed spike-based sub-λ neuromorphic nanophotonic computing

2020

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AbstractEvent-activated biological-inspired subwavelength (sub-λ) photonic neural networks are of key importance for future energy-efficient and high-bandwidth artificial intelligence systems. However, a miniaturized light-emitting nanosource for spike-based operation of interest for neuromorphic optical computing is still lacking. In this work, we propose and theoretically analyze a novel nanoscale nanophotonic neuron circuit. It is formed by a quantum resonant tunneling (QRT) nanostructure monolithic integrated into a sub-λ metal-cavity nanolight-emitting diode (nanoLED). The resulting optical nanosource displays a negative differential conductance which controls the all-or-nothing optical spiking response of the nanoLED. Here we demonstrate efficient activation of the spiking response via high-speed nonlinear electrical modulation of the nanoLED. A model that combines the dynamical equations of the circuit which considers the nonlinear voltage-controlled current characteristic, and rate equations that takes into account the Purcell enhancement of the spontaneous emission, is used to provide a theoretical framework to investigate the optical spiking dynamic properties of the neuromorphic nanoLED. We show inhibitory- and excitatory-like optical spikes at multi-gigahertz speeds can be achieved upon receiving exceptionally low (sub-10 mV) synaptic-like electrical activation signals, lower than biological voltages of 100 mV, and with remarkably low energy consumption, in the range of 10–100 fJ per emitted spike. Importantly, the energy per spike is roughly constant and almost independent of the incoming modulating frequency signal, which is markedly different from conventional current modulation schemes. This method of spike generation in neuromorphic nanoLED devices paves the way for sub-λ incoherent neural elements for fast and efficient asynchronous neural computation in photonic spiking neural networks.

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Section: Neuromorphic Nanoled Devicementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

NanoLEDs for energy-efficient and gigahertz-speed spike-based sub-λ neuromorphic nanophotonic computing

2020

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“…Importantly, the DBQW can be epitaxially integrated with other active materials (either based on III-V or III-N), and RTD-based photodetectors and light sources have been demonstrated. Notable examples include single-photon switches and detectors [37,38], near-infrared photodetectors for optical communications [62], mid-infrared detectors for sensing [63], III-nitride LED sources [64], III-V unipolar (n-type) bistable light-emitting RTDs [65], bipolar (p-n-type) RTD-based LEDs [66][67][68] and RTD-lasers [69,70]. These works open unique opportunities to combine the nonlinear electrical properties of RTDs with the optical characteristics of photonic devices to create novel architectures of interest for emergent neuromorphic optical computing systems.…”

Section: Nanortd Neuronsmentioning

confidence: 99%

Brain-inspired nanophotonic spike computing: challenges and prospects

Romeira

Adão

Nieder

et al. 2023

Neuromorph. Comput. Eng.

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Nanophotonic spiking neural networks based on neuron-like excitable subwavelength (submicrometre) devices are of key importance for realizing brain-inspired, power-efficient artificial intelligence (AI) systems with high degree of parallelism and energy efficiency. Despite significant advances in neuromorphic photonics, compact and efficient nanophotonic elements for spiking signal emission and detection, as required for spike-based computation, remain largely unexplored. In this invited perspective, we outline the main challenges, early achievements, and opportunities toward a key-enabling photonic neuro-architecture using III-V/Si integrated spiking nodes based on nanoscale resonant tunnelling diodes (nanoRTDs). We utilize nanoRTDs as nonlinear artificial neurons capable of spiking at high-speeds. We discuss the prospects for monolithic integration of nanoRTDs with nanoscale light-emitting diodes (nanoLEDs), nanolaser diodes (nanoLDs), and nanophotodetectors (nanoPDs) to realize neuron emitter and receiver spiking nodes. Such layout would have a small footprint, fast operation, and low power consumption, all key requirements for efficient optoelectronic spiking operation. We discuss how silicon photonics interconnects, integrated photorefractive interconnects, and 3D waveguide polymeric interconnections can be used for interconnecting the emitter-receiver spiking photonic neural nodes. Finally, using numerical simulations of artificial neuron models, we present spike-based spatio-temporal learning methods for applications in relevant functional AI tasks, such as image pattern recognition, edge detection, and spiking neural networks for inference and learning. Future developments in neuromorphic spiking photonic nanocircuits, as outlined here, will significantly boost the processing and transmission capabilities of next-generation nanophotonic spike-based neuromorphic architectures for energy-efficient AI applications.

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“…18 to 5 Â 10 18 ions/ cm 2 ) at various temperatures ranging from T H ¼ 573 K to 613 K and times t H from 2 to 5 h. Following preliminary studies of the effects of hydrogen on the resistivity of the p-type contact layer, relatively low hydrogen doses (d H ¼ 1 Â 10 18 ions/cm 2 ) were chosen to minimize the H-induced neutralization of the acceptors in the top p-contact GaAs layer. 13 On the other hand, as discussed below, these doses proved to be appropriate for the diffusion of hydrogen into the GaAs 1Àx N x /AlAs SL.…”

mentioning

confidence: 95%

Tunable spectral response by hydrogen irradiation of Ga(AsN) superlattice diodes

Balakrishnan

Pettinari

Makarovsky

et al. 2014

Applied Physics Letters

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We report on the tuning of the spectral response of superlattice (SL) diodes based on dilute nitride Ga(AsN) alloys by post-growth hydrogenation. Hydrogen is incorporated into the superlattice where it neutralizes the electronic activity of nitrogen by forming N-H complexes. We exploit the controlled thermal dissociation of the complexes to tune the energy of the SL photocurrent absorption and electroluminescence emission; also, by annealing a submicron spot with a focused laser beam we create a preferential path for the injection of carriers, thus activating a nanoscale light emitting region. This method can be used for fabricating planar diode arrays with distinct optical active regions, all integrated onto a single substrate.

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A micrometer-size movable light emitting area in a resonant tunneling light emitting diode

Cited by 3 publications

References 22 publications

NanoLEDs for energy-efficient and gigahertz-speed spike-based sub-λ neuromorphic nanophotonic computing

NanoLEDs for energy-efficient and gigahertz-speed spike-based sub-λ neuromorphic nanophotonic computing

Brain-inspired nanophotonic spike computing: challenges and prospects

Tunable spectral response by hydrogen irradiation of Ga(AsN) superlattice diodes

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