We demonstrate nonlinear metamaterial split ring resonators (SRRs) on GaAs at terahertz frequencies. For SRRs on doped GaAs films, incident terahertz radiation with peak fields of ~20-160 kV/cm ...drives intervalley scattering. This reduces the carrier mobility and enhances the SRR LC response due to a conductivity decrease in the doped thin film. Above ~160 kV/cm, electric field enhancement within the SRR gaps leads to efficient impact ionization, increasing the carrier density and the conductivity which, in turn, suppresses the SRR resonance. We demonstrate an increase of up to 10 orders of magnitude in the carrier density in the SRR gaps on semi-insulating GaAs. Furthermore, we show that the effective permittivity can be swept from negative to positive values with an increasing terahertz field strength in the impact ionization regime, enabling new possibilities for nonlinear metamaterials.
Measurements on graphene exfoliated over a substrate prepatterned with shallow depressions demonstrate that graphene does not remain free-standing but instead adheres to the substrate despite the ...induced biaxial strain. The strain is homogeneous over the depression bottom as determined by Raman measurements. We find higher Raman shifts and Grüneisen parameters of the phonons underlying the G and 2D bands under biaxial strain than previously reported. Interference modeling is used to determine the vertical position of the graphene and to calculate the optimum dielectric substrate stack for maximum Raman signal.
We investigate transient nanotextured heterogeneity in vanadium dioxide (VO2) thin films during a light-induced insulator-to-metal transition (IMT). Time-resolved scanning near-field optical ...microscopy (Tr-SNOM) is used to study VO2 across a wide parameter space of infrared frequencies, picosecond time scales, and elevated steady-state temperatures with nanoscale spatial resolution. Room temperature, steady-state, phonon enhanced nano-optical contrast reveals preexisting “hidden” disorder. The observed contrast is associated with inequivalent twin domain structures. Upon thermal or optical initiation of the IMT, coexisting metallic and insulating regions are observed. Correlations between the transient and steady-state nano-optical textures reveal that heterogeneous nucleation is partially anchored to twin domain interfaces and grain boundaries. Ultrafast nanoscopic dynamics enable quantification of the growth rate and bound the nucleation rate. Finally, we deterministically anchor photoinduced nucleation to predefined nanoscopic regions by locally enhancing the electric field of pump radiation using nanoantennas and monitor the on-demand emergent metallicity in space and time.
C^N^: Complex-Valued Contourlet Neural Network Liu, Mengkun; Jiao, Licheng; Liu, Xu ...
IEEE journal of selected topics in applied earth observations and remote sensing,
2024, Volume:
17
Journal Article
Peer reviewed
Open access
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 (C<inline-formula><tex-math notation="LaTeX">^{2}</tex-math></inline-formula>N<inline-formula><tex-math notation="LaTeX">^{2}</tex-math></inline-formula>) 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 C<inline-formula><tex-math notation="LaTeX">^{2}</tex-math></inline-formula>N<inline-formula><tex-math notation="LaTeX">^{2}</tex-math></inline-formula> 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 C<inline-formula><tex-math notation="LaTeX">^{2}</tex-math></inline-formula>N<inline-formula><tex-math notation="LaTeX">^{2}</tex-math></inline-formula> in deep learning, recognition, and interpretation.
Abstract
Ferroelectricity, a spontaneous and reversible electric polarization, is found in certain classes of van der Waals (vdW) materials. The discovery of ferroelectricity in twisted vdW layers ...provides new opportunities to engineer spatially dependent electric and optical properties associated with the configuration of moiré superlattice domains and the network of domain walls. Here, we employ near-field infrared nano-imaging and nano-photocurrent measurements to study ferroelectricity in minimally twisted WSe
2
. The ferroelectric domains are visualized through the imaging of the plasmonic response in a graphene monolayer adjacent to the moiré WSe
2
bilayers. Specifically, we find that the ferroelectric polarization in moiré domains is imprinted on the plasmonic response of the graphene. Complementary nano-photocurrent measurements demonstrate that the optoelectronic properties of graphene are also modulated by the proximal ferroelectric domains. Our approach represents an alternative strategy for studying moiré ferroelectricity at native length scales and opens promising prospects for (opto)electronic devices.
Abstract
Ultrafast control of structural and electronic properties of various quantum materials has recently sparked great interest. In particular, photoinduced switching between distinct topological ...phases has been considered a promising route to realize quantum computers. Here we use first-principles and effective Hamiltonian methods to show that in ZrTe
5
, lattice distortions corresponding to all three types of zone-center infrared optical phonon modes can drive the system from a topological insulator to a Weyl semimetal. Thus achieved Weyl phases are robust, highly tunable, and one of the cleanest due to the proximity of the Weyl points to the Fermi level and a lack of other carriers. We also find that Berry curvature dipole moment, induced by the dynamical inversion symmetry breaking, gives rise to various nonlinear effects that oscillate with the amplitude of the phonon modes. These nonlinear effects present an ultrafast switch for controlling the Weyltronics-enabled quantum system.
Broadband tunability is a central theme in contemporary nanophotonics and metamaterials research. Combining metamaterials with phase change media offers a promising approach to achieve such ...tunability, which requires a comprehensive investigation of the electromagnetic responses of novel materials at subwavelength scales. In this work, we demonstrate an innovative way to tailor band-selective electromagnetic responses at the surface of a heavy fermion compound, samarium sulfide (SmS). By utilizing the intrinsic, pressure sensitive, and multi-band electron responses of SmS, we create a proof-of-principle heavy fermion metamaterial, which is fabricated and characterized using scanning near-field microscopes with <50 nm spatial resolution. The optical responses at the infrared and visible frequency ranges can be selectively and separately tuned via modifying the occupation of the 4f and 5d band electrons. The unique pressure, doping, and temperature tunability demonstrated represents a paradigm shift for nanoscale metamaterial and metasurface design.
Precise patterning of biomaterials has widespread applications, including drug release, degradable implants, tissue engineering, and regenerative medicine. Patterning of protein‐based microstructures ...using UV‐photolithography has been demonstrated using protein as the resist material. The Achilles heel of existing protein‐based biophotoresists is the inevitable wide molecular weight distribution during the protein extraction/regeneration process, hindering their practical uses in the semiconductor industry where reliability and repeatability are paramount. A wafer‐scale high resolution patterning of bio‐microstructures using well‐defined silk fibroin light chain as the resist material is presented showing unprecedent performances. The lithographic and etching performance of silk fibroin light chain resists are evaluated systematically and the underlying mechanisms are thoroughly discussed. The micropatterned silk structures are tested as cellular substrates for the successful spatial guidance of fetal neural stems cells seeded on the patterned substrates. The enhanced patterning resolution, the improved etch resistance, and the inherent biocompatibility of such protein‐based photoresist provide new opportunities in fabricating large scale biocompatible functional microstructures.
The Achilles heel of natural silk fibroin‐based resists is the wide molecular weight distribution which hinders their applications. To solve this issue, the light chain is isolated from the silk fibroin and subsequently conjugated by photo‐crosslinkers to synthesize the photoreactive bioresist. A wafer‐scale patterning of bio‐microstructures with high resolution, enhanced etching resistance, and the inherent biocompatibility is achieved using such precise protein photolithography.
The ability to manipulate the metal–insulator transition (MIT) of metal oxides is of critical importance for fundamental investigations of electron correlations and practical implementations of power ...efficient tunable electrical and optical devices. Most of the existing techniques including chemical doping and epitaxial strain modification can only modify the global transition temperature, while the capability to locally manipulate MIT is still lacking for developing highly integrated functional devices. Here, lattice engineering induced by the energetic noble gas ion allowing a 3D local manipulation of the MIT in VO2 films is demonstrated and a spatial resolution laterally within the micrometer scale is reached. Ion‐induced open volume defects efficiently modify the lattice constants of VO2 and consequently reduce the MIT temperature continuously from 341 to 275 K. According to a density functional theory calculation, the effect of lattice constant variation reduces the phase change energy barrier and therefore triggers the MIT at a much lower temperature. VO2 films with multiple transitions in both in‐plane and out‐of‐plane dimensions can be achieved by implantation through a shadow mask or multienergy implantation. Based on this method, temperature‐controlled VO2 metasurface structure is demonstrated by tuning only locally the MIT behavior on the VO2 surfaces.
The metal–insulator transition (MIT) temperature of VO2 thin film is dramatically reduced by noble gas ion implantation. VO2 film with multi‐MIT processes in both in‐plane and out‐of‐plane dimensions is achieved by implantation through a patterned surface or by multienergy implantation, which allows to manipulate the phase transition process of VO2 film at any site in 3D space.
Extracting effective features is always a challenging problem for texture classification because of the uncertainty of scales and the clutter of textural patterns. For texture classification, ...spectral analysis is traditionally employed in the frequency domain. Recent studies have shown the potential of convolutional neural networks (CNNs) when dealing with the texture classification task in the spatial domain. In this article, we try combining both approaches in different domains for more abundant information and proposed a novel network architecture named contourlet CNN (C-CNN). The network aims to learn sparse and effective feature representations for images. First, the contourlet transform is applied to get the spectral features from an image. Second, the spatial-spectral feature fusion strategy is designed to incorporate the spectral features into CNN architecture. Third, the statistical features are integrated into the network by the statistical feature fusion. Finally, the results are obtained by classifying the fusion features. We also investigated the behavior of the parameters in contourlet decomposition. Experiments on the widely used three texture data sets (kth-tips2-b, DTD, and CUReT) and five remote sensing data sets (UCM, WHU-RS, AID, RSSCN7, and NWPU-RESISC45) demonstrate that the proposed approach outperforms several well-known classification methods in terms of classification accuracy with fewer trainable parameters.
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