Nonlinear coupled-mode-theory framework for graphene-induced saturable absorption in nanophotonic resonant structures

Ataloglou, Vasileios G.; Christopoulos, Thomas; Kriezis, Emmanouil E.

doi:10.1103/physreva.97.063836

Cited by 17 publications

(21 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…𝜏 𝑆𝐴 [36], where 𝜏 𝑒 and 𝜏 𝑆𝐴 are the photon resonator lifetimes corresponding to the coupling and resistive losses, respectively. The variable lifetime 𝜏 𝑆𝐴 depends on the energy in the cavity:…”

Section: )mentioning

confidence: 99%

Resonance for Analog Recurrent Neural Network

Zhou

Khoram

et al. 2022

ACS Photonics

View full text Add to dashboard Cite

There is a strong interest in using physical waves for artificial neural computing because of their unique advantages in fast speed and intrinsic parallelism. Resonance, as a ubiquitous feature across many wave systems, is a natural candidate for analog computing in temporal signals. We demonstrate that resonance can be used to construct stable and scalable recurrent neural networks. By including resonators with different lifetimes, the computing system develops both short-term and long-term memory simultaneously.

show abstract

Section: )mentioning

confidence: 99%

Resonance for Analog Recurrent Neural Network

Zhou

Khoram

et al. 2022

ACS Photonics

View full text Add to dashboard Cite

show abstract

“…σ FCD is related to the FCD effect, γ FCA and α TPA is, respectively, the FCA absorption coefficient and TPA coefficient. W sat is the total energy stored in the cavity at the onset of saturable absorption [55,56]. τ f c is the normalized carrier lifetime, and n is the normalized carrier density.…”

Section: Mrr-based Photonic Neuronmentioning

confidence: 99%

An All-MRR-Based Photonic Spiking Neural Network for Spike Sequence Learning

et al. 2022

View full text Add to dashboard Cite

Photonic spiking neural networks (SNN) have the advantages of high power efficiency, high bandwidth and low delay, but limitations are encountered in large-scale integration. The silicon photonics platform is a promising candidate for realizing large-scale photonic SNN because it is compatible with the current mature CMOS platforms. Here, we present an architecture of photonic SNN which consists of photonic neuron, photonic spike timing dependent plasticity (STDP) and weight configuration that are all based on silicon micro-ring resonators (MRRs), via taking advantage of the nonlinear effects in silicon. The photonic spiking neuron based on the add-drop MRR is proposed, and a system-level computational model of all-MRR-based photonic SNN is presented. The proposed architecture could exploit the properties of small area, high integration and flexible structure of MRR, but also faces challenges caused by the high sensitivity of MRR. The spike sequence learning problem is addressed based on the proposed all-MRR-based photonic SNN architecture via adopting supervised training algorithms. We show the importance of algorithms when hardware devices are limited.

show abstract

“…Consequently, the optical response of graphene metasurfaces can be highly isotropic, when the geometry of graphene unit cell is symmetric. In fact, patterned graphene has already been employed in the design of terahertz devices, such as perfect absorbers, filters, and tunable reflectors [38][39][40][41][42][43] . In this context, a particularly appealing physical property of graphene is the tunability of its dielectric constant, a unique functionality that is highly relevant to the design of active photonic devices.…”

Section: Introductionmentioning

confidence: 99%

Large enhancement of the effective second-order nonlinearity in graphene metasurfaces

2019

View full text Add to dashboard Cite

Using a powerful homogenization technique, one-and two-dimensional graphene metasurfaces are homogenized both at the fundamental frequency (FF) and second-harmonic (SH). In both cases, there is excellent agreement between the predictions of the homogenization method and those based on rigorous numerical solutions of Maxwell equations. The homogenization technique is then employed to demonstrate that, owing to a double-resonant plasmon excitation mechanism that leads to strong, simultaneous field enhancement at the FF and SH, the effective second-order susceptibility of graphene metasurfaces can be enhanced by more than three orders of magnitude as compared to the intrinsic second-order susceptibility of a graphene sheet placed on the same substrate. In addition, we explore the implications of our results to the development of new active nanodevices that incorporate nanopatterned graphene structures. arXiv:1905.06497v1 [physics.optics]

show abstract

Nonlinear coupled-mode-theory framework for graphene-induced saturable absorption in nanophotonic resonant structures

Cited by 17 publications

References 48 publications

Resonance for Analog Recurrent Neural Network

Resonance for Analog Recurrent Neural Network

An All-MRR-Based Photonic Spiking Neural Network for Spike Sequence Learning

Large enhancement of the effective second-order nonlinearity in graphene metasurfaces

Contact Info

Product

Resources

About