Manipulation of the propagation and energy‐transport characteristics of subwavelength infrared (IR) light fields is critical for the application of nanophotonic devices in photocatalysis, biosensing, ...and thermal management. In this context, metamaterials are useful composite materials, although traditional metal‐based structures are constrained by their weak mid‐IR response, while their associated capabilities for optical propagation and focusing are limited by the size of attainable artificial optical structures and the poor performance of the available active means of control. Herein, a tunable planar focusing device operating in the mid‐IR region is reported by exploiting highly oriented in‐plane hyperbolic phonon polaritons in α‐MoO3. Specifically, an unprecedented change of effective focal length of polariton waves from 0.7 to 7.4 μm is demonstrated by the following three different means of control: the dimension of the device, the employed light frequency, and engineering of phonon–plasmon hybridization. The high confinement characteristics of phonon polaritons in α‐MoO3 permit the focal length and focal spot size to be reduced to 1/15 and 1/33 of the incident wavelength, respectively. In particular, the anisotropic phonon polaritons supported in α‐MoO3 are combined with tunable surface‐plasmon polaritons in graphene to realize in situ and dynamical control of the focusing performance, thus paving the way for phonon‐polariton‐based planar nanophotonic applications.
A planar polariton focusing device based on the natural in‐plane hyperbolic van der Waals material α‐MoO3 is developed, which achieves ultrahigh field compression and wide‐range tunable performance. Moreover, a graphene/α‐MoO3 heterostructure is constructed, which supports hybrid modes consisting of α‐MoO3 phonon polaritons and graphene plasmons to achieve in situ dynamical control of the focal length.
Unlike conventional plasmonic media, polaritonic van der Waals (vdW) materials hold promise for active control of light-matter interactions. The dispersion relations of elementary excitations such as ...phonons and plasmons can be tuned in layered vdW systems via stacking using functional substrates. In this work, infrared nanoimaging and nanospectroscopy of hyperbolic phonon polaritons are demonstrated in a novel vdW heterostructure combining hexagonal boron nitride (hBN) and vanadium dioxide (VO
). It is observed that the insulator-to-metal transition in VO
has a profound impact on the polaritons in the proximal hBN layer. In effect, the real-space propagation of hyperbolic polaritons and their spectroscopic resonances can be actively controlled by temperature. This tunability originates from the effective change in local dielectric properties of the VO
sublayer in the course of the temperature-tuned insulator-to-metal phase transition. The high susceptibility of polaritons to electronic phase transitions opens new possibilities for applications of vdW materials in combination with strongly correlated quantum materials.
Complex-valued convolutional neural networks (CV-CNN) have recently gained recognition in feature representation learning. It implements the repeated application of the operations in convolution, ...local average pooling, and the absolute value of the resulting vectors. However, it is only conducted in the complex spatial domain, and lacks effective representation of directionality, singularity, and regularity in the complex spectral domain for anomaly detection of images. This is the key to feature learning representation of high-order singularity. To solve this problem, a complex-valued contourlet neural network (CFormula OmittedNFormula Omitted) is proposed in this article. It is novel in this sense that, different from the CV-CNN in the spatial domain, the spectral stream of CFormula OmittedNFormula Omitted can enhance the multiresolution sparse representation of nonsubsampled contourlet (NSCT) with multiscales and multidirections for images. Furthermore, the spectral feature integration module is proposed to capture the statistical properties of the NSCT coefficients. It is shown that the proposed network can improve the distinguishability of feature learning and classification ability in theoretical analysis and experiments on three benchmark datasets (Flevoland, Xi'an, and Germany) compared with developed methods. Polarimetric synthetic aperture radar image classification is widely used in the fields of agriculture, forestry, and military. It must be emphasized that there is potential in effective feature learning representation and the generalization capability of CFormula OmittedNFormula Omitted in deep learning, recognition, and interpretation.
Over the past decade, breakthroughs in the generation and control of ultrafast high-field terahertz (THz) radiation have led to new spectroscopic methodologies for the study of light-matter ...interactions in the strong-field limit. In this review, we will outline recent experimental demonstrations of non-linear THz material responses in materials ranging from molecular gases, to liquids, to varieties of solids - including semiconductors, nanocarbon, and correlated electron materials. New insights into how strong THz fields interact with matter will be discussed in which a THz field can act as either a non-resonant electric field or a broad bandwidth pulse driving specific resonances within it. As an emerging field, non-linear THz spectroscopy shows promise for elucidating dynamic problems associated with next generation electronics and optoelectronics, as well as for demonstrating control over collective material degrees of freedom.
The ability to perform nanometer‐scale optical imaging and spectroscopy is key to deciphering the low‐energy effects in quantum materials, as well as vibrational fingerprints in planetary and ...extraterrestrial particles, catalytic substances, and aqueous biological samples. These tasks can be accomplished by the scattering‐type scanning near‐field optical microscopy (s‐SNOM) technique that has recently spread to many research fields and enabled notable discoveries. Herein, it is shown that the s‐SNOM, together with scanning probe research in general, can benefit in many ways from artificial‐intelligence (AI) and machine‐learning (ML) algorithms. Augmented with AI‐ and ML‐enhanced data acquisition and analysis, scanning probe optical nanoscopy is poised to become more efficient, accurate, and intelligent.
Three main paradigms of machine learning—supervised learning, unsupervised learning, and reinforcement learning—can be applied to optical scanning probe techniques in future instrumentation and data analysis to solve unique problems.
The development of electronics and photonics is entering a new era of ultrahigh speed sensing, data processing, and telecommunication. The carrier frequencies of the next‐generation electronic ...devices inevitably extend beyond radio frequencies, marching toward the nominally photonics‐dominated territories, e.g., terahertz and beyond. As a result, electronic and photonic techniques naturally merge and seek common ground. At the forefront of this technical trend is the field of polaritonics, where polaritons are half‐light, half‐matter quasiparticles that carry the properties of both “bare” photons and “bare” dipole‐carrying excitations. The Janus‐faced nature of polaritons renders the unique capability of operando control using photoexcitation or applied electric field. Here, state‐of‐the‐art ultrafast polaritonic phenomena probed by scattering‐type scanning near‐field optical microscope (s‐SNOM) techniques is reviewed. The ultrafast dynamical control and loss‐reduction of the polariton propagation are discussed with special emphasis on the creation and probing of the tip or edge induced plasmon– and phonon–polaritons in low‐dimensional systems. The detailed technical aspects of s‐SNOM and its possible future development are also presented.
A review on the principle and recent progress in the field of real‐space imaging and spectroscopy of various types of polaritons using scattering‐type scanning near‐field optical microscopy (s‐SNOM) are provided here, with a special focus on time‐resolved experiments. New directions for the s‐SNOM and polaritonic research, especially the study of polaritonic dynamics at nanoscale are also presented.
Unlike conventional plasmonic media, polaritonic van der Waals (vdW) materials hold promise for active control of light–matter interactions. The dispersion relations of elementary excitations such as ...phonons and plasmons can be tuned in layered vdW systems via stacking using functional substrates. In this work, infrared nanoimaging and nanospectroscopy of hyperbolic phonon polaritons are demonstrated in a novel vdW heterostructure combining hexagonal boron nitride (hBN) and vanadium dioxide (VO2). It is observed that the insulator‐to‐metal transition in VO2 has a profound impact on the polaritons in the proximal hBN layer. In effect, the real‐space propagation of hyperbolic polaritons and their spectroscopic resonances can be actively controlled by temperature. This tunability originates from the effective change in local dielectric properties of the VO2 sublayer in the course of the temperature‐tuned insulator‐to‐metal phase transition. The high susceptibility of polaritons to electronic phase transitions opens new possibilities for applications of vdW materials in combination with strongly correlated quantum materials.
In van der Waals heterostructures comprising hexagonal boron nitride (hBN) and vanadium dioxide (VO2), dynamic and reversible tuning of hyperbolic phonon polaritons is achieved via the insulator‐to‐metal phase transition by controlling the temperature. Using infrared nanospectroscopy, opposite tuning trends for in‐plane and out‐of‐plane phonon resonances are demonstrated during the phase transition.
Abstract
The ability to perform nanometer‐scale optical imaging and spectroscopy is key to deciphering the low‐energy effects in quantum materials, as well as vibrational fingerprints in planetary ...and extraterrestrial particles, catalytic substances, and aqueous biological samples. These tasks can be accomplished by the scattering‐type scanning near‐field optical microscopy (s‐SNOM) technique that has recently spread to many research fields and enabled notable discoveries. Herein, it is shown that the s‐SNOM, together with scanning probe research in general, can benefit in many ways from artificial‐intelligence (AI) and machine‐learning (ML) algorithms. Augmented with AI‐ and ML‐enhanced data acquisition and analysis, scanning probe optical nanoscopy is poised to become more efficient, accurate, and intelligent.
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