Carbon-based terahertz devices

He, Xiaowei; Gao, Weilu; Zhang, Qi; Ren, Lei; Kono, Junichiro

doi:10.1117/12.2185159

Cited by 8 publications

(7 citation statements)

References 49 publications

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“…At infrared frequencies, nanotube plasmonics could lead to highly sensitive absorption spectroscopy through surface enhanced infrared absorption (SEIRA) [11][12][13]. At terahertz frequencies, it could enable tunable lasers and receivers for use in terabit-per-second wireless communications [14][15][16]. Ultra-broadband nanotube plasmonic circuitry could be naturally integrated with high-performance nanotube transistors.…”

Section: Main Textmentioning

confidence: 99%

“…At terahertz frequencies, it could enable tunable lasers and receivers for use in terabit-per-second wireless communications [14][15][16]. Ultra-broadband nanotube plasmonic circuitry could be naturally integrated with high-performance nanotube transistors.…”

Section: Main Textmentioning

confidence: 99%

“…The intense concentration of electromagnetic fields deriving from the nanotubes' one dimensionality allows the plasmons both to confine light to the nanometer scale and to enhance light-matter interactions by Purcell factors that are predicted to be as high as 10 6 [5].At infrared frequencies, nanotube plasmonics could lead to highly sensitive absorption spectroscopy through surface enhanced infrared absorption (SEIRA) [11][12][13]. At terahertz frequencies, it could enable tunable lasers and receivers for use in terabit-per-second wireless communications [14][15][16]. Ultra-broadband nanotube plasmonic circuitry could be naturally integrated with high-performance nanotube transistors.…”

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confidence: 99%

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Coherent Plasmon and Phonon-Plasmon Resonances in Carbon Nanotubes

Falk¹,

Chiu

Farmer³

et al. 2017

Phys. Rev. Lett.

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Carbon nanotubes provide a rare access point into the plasmon physics of one-dimensional electronic systems. By assembling purified nanotubes into uniformly sized arrays, we show that they support coherent plasmon resonances, that these plasmons enhance and hybridize with phonons, and that the phonon-plasmon resonances have quality factors as high as 10. Because coherent nanotube plasmonics can strengthen light-matter interactions, it provides a compelling platform for surface-enhanced infrared spectroscopy and tunable, high-performance optical devices at the nanometer scale. Main textPlasmons in carbon nanotubes [1][2][3][4][5] comprise longitudinal charge oscillations coupled to infrared or terahertz optical fields. They can either propagate [3][4][5] or be confined to FabryPérot resonators by reflections at the nanotube ends [6][7][8][9][10] (Fig. 1(a)). Propagation losses are low [3], and the resonant frequencies and absorption coefficients can be controlled via the length [8,10] and doping level [7,9] of the nanotubes. The intense concentration of electromagnetic fields deriving from the nanotubes' one dimensionality allows the plasmons both to confine light to the nanometer scale and to enhance light-matter interactions by Purcell factors that are predicted to be as high as 10 6 [5].At infrared frequencies, nanotube plasmonics could lead to highly sensitive absorption spectroscopy through surface enhanced infrared absorption (SEIRA) [11][12][13]. At terahertz frequencies, it could enable tunable lasers and receivers for use in terabit-per-second wireless communications [14][15][16]. Ultra-broadband nanotube plasmonic circuitry could be naturally integrated with high-performance nanotube transistors.However, nanotube plasmonics has been frustrated by the material quality of nanotube films. In inhomogeneous nanotube films, with a broad distribution of lengths, diameters and/or doping levels, plasmons resonating at different frequencies quickly lose phase coherence with each other, leading to fast dissipation. The quality (Q) factor, which is the quotient of the resonant angular frequency and the dissipation rate, is therefore low. To date, this inhomogeneity has limited observations of nanotube plasmon resonators to the incoherent Q ≪ 1 regime [6][7][8][9][10]. Because dissipation constrains nearly all applications of plasmonics, the demonstration of high Q * Contact: alfalk@us.ibm.com 2 resonators would provide crucial evidence that nanotubes are a technologically viable plasmonic material.In this work, we show that coherent nanotube plasmon and phonon-plasmon resonances can have ensemble Q factors as high as 10. The key to our demonstration is our exceptionally uniform nanotube films, which we develop using Langmuir-Schaeffer techniques [17]. We conservatively estimate that our nanotube resonators confine an electromagnetic field whose free-space wavelength (λ0) is 8 µm to a mode volume (V) of 0.002 µm 3 . With this combination of Q and optical concentration (λ / = 300,000), the Purcell factor by w...

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Section: Main Textmentioning

confidence: 99%

Section: Main Textmentioning

confidence: 99%

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confidence: 99%

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Coherent Plasmon and Phonon-Plasmon Resonances in Carbon Nanotubes

Falk¹,

Chiu

Farmer³

et al. 2017

Phys. Rev. Lett.

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“…At the time of this writing, a first pioneering attempt has been made to fit superparameterized convection in a realistic operational setting. Results of this study indicate that sophisticated network designs involving the addition of 1D convolutions in the vertical dimension, and "residual" neural network architecture (He et al, 2015) using state information from previous time steps, appeared critical to achieving reasonable fits (Han et al, 2020). This raises two issues.…”

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confidence: 98%

Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models With Real‐Geography Boundary Conditions

Griffin

Pritchard

Beucler

et al. 2021

J Adv Model Earth Syst

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Although global atmospheric model simulations are increasingly high-resolution, even under optimistic scenarios of enhanced computing performance, physically resolving the atmospheric turbulence controlling clouds will likely not be feasible for decades. Current climate model horizontal grid cells are typically 50-100 km wide but the turbulent updrafts governing cloud formation occur on scales of just tens to hundreds of meters and the microphysical processes regulating convection occur down at the micrometer Abstract We explore the potential of feed-forward deep neural networks (DNNs) for emulating cloud superparameterization in realistic geography, using offline fits to data from the superparameterized community atmospheric model. To identify the network architecture of greatest skill, we formally optimize hyperparameters using ∼250 trials. Our DNN explains over 70% of the temporal variance at the 15-min sampling scale throughout the mid-to-upper troposphere. Autocorrelation timescale analysis compared against DNN skill suggests the less good fit in the tropical, marine boundary layer is driven by neural network difficulty emulating fast, stochastic signals in convection. However, spectral analysis in the temporal domain indicates skillful emulation of signals on diurnal to synoptic scales. A closer look at the diurnal cycle reveals correct emulation of land-sea contrasts and vertical structure in the heating and moistening fields, but some distortion of precipitation. Sensitivity tests targeting precipitation skill reveal complementary effects of adding positive constraints versus hyperparameter tuning, motivating the use of both in the future. A first attempt to force an offline land model with DNN emulated atmospheric fields produces reassuring results further supporting neural network emulation viability in realgeography settings. Overall, the fit skill is competitive with recent attempts by sophisticated Residual and Convolutional Neural Network architectures trained on added information, including memory of past states. Our results confirm the parameterizability of superparameterized convection with continents through machine learning and we highlight the advantages of casting this problem locally in space and time for accurate emulation and hopefully quick implementation of hybrid climate models.Plain Language Summary Machine learning methods have been previously used to replace parameterizations (approximations) of atmospheric convection under very idealized scenarios (aqua-planets). The hope is that these machine learning emulators can help power the next generation of climate models with similar accuracy but at a fraction of the computational cost. But important questions remain about how learnable more realistic convection (over both land and ocean) is. Recently, the first attempt at machine learning replicated convection was made under these Earth-like conditions. But it required a highly specialized neural network as well as memory of the previous behavior of the atmosphere. This design would make ...

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“…[ 109 ] More specifically, the large mean free path and extraordinary electronic properties of CNTs make them particularly suitable for detecting terahertz modulations. [ 110 ] Nikolaenko et al. reported the exceptional nonlinear optical properties of a CNT metamaterial in the terahertz spectral range ( Figure a).…”

Section: From Elemental To Hybrid Tms Materialsmentioning

confidence: 99%

Recent Advances in the Development of Materials for Terahertz Metamaterial Sensing

Shen

et al. 2021

Advanced Optical Materials

169

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Terahertz metamaterial sensing (TMS) is a new interdisciplinary technology. A TMS system employs terahertz waves as the pumping source, these then interact with the sample and carry the substance information, e.g., refractive index, absorption spectra. These properties are relevant to the molecular rotation and vibration states produced by a surface‐plasmon‐polariton‐like effect. TMS technology is usually characterized by large penetration depth and high sensitivity. Owing to these advantages, TMS may be used for ultratrace detection and consequently has a wide range of practical applications in biomedicine, food safety, environmental monitoring, industry and agriculture, material characterization, and safety inspection. Furthermore, TMS performance is determined not only by the structural topology of metamaterials, but also by their compositions and substrates. This paper reviews the essential fundamentals, relevant applications, and recent advances in TMS technology with a focus on the influence of material selection on TMS performance. This review is envisaged to be used as a key reference for developing TMS‐based functional devices with enhanced characteristics.

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Carbon-based terahertz devices

Cited by 8 publications

References 49 publications

Coherent Plasmon and Phonon-Plasmon Resonances in Carbon Nanotubes

Coherent Plasmon and Phonon-Plasmon Resonances in Carbon Nanotubes

Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models With Real‐Geography Boundary Conditions

Recent Advances in the Development of Materials for Terahertz Metamaterial Sensing

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