Dichromatic "Breather Molecules" in a Mode-Locked Fiber Laser.

Bound states of solitons ("molecules") occur in various settings, playing an important role in the operation of fiber lasers, optical emulation, encoding, and communications. Soliton interactions are generally related to breathing dynamics in nonlinear dissipative systems, and maintain potential applications in spectroscopy. In the present work, dichromatic breather molecules (DBMs) are created in a synchronized mode-locked fiber laser. Real-time delay-shifting interference spectra are measured to display the temporal evolution of the DBMs, that cannot be observed by means of the usual real-time spectroscopy. As a result, robust out-of-phase vibrations are found as a typical intrinsic mode of DBMs. The same bound states are produced numerically in the framework of a model combining equations for the population inversion in the mode-locked laser and cross-phase-modulation-coupled complex Ginzburg-Landau equations for amplitudes of the optical fields in the fiber segments of the laser cavity. The results demonstrate that the Q-switching instability induces the onset of breathing oscillations. The findings offer new possibilities for the design of various regimes of the operation of ultrafast lasers.

High-performance full-color imaging system based on end-to-end joint optimization of computer-generated holography and metalens.

Metasurface has drawn extensive attention due to its capability of modulating light with a high degree of freedom through ultrathin and sub-wavelength optical elements, and metalens, as one of its important applications, promises to replace the bulky refractive optics, facilitating the imaging system light-weight and compact characteristics. Besides, computer-generated holography (CGH) is of substantial interest for three-dimensional (3D) imaging technology by virtue of its ability of restoring the whole optical wave field and re-constructing the true 3D scene. Consequently, the combination of metalens and CGH holds transformative potential in enabling the miniaturization of 3D imaging systems. However, its imaging performance is subject to the aberrations and speckle noises originating from the metalens and CGH. Inspired by recent progress that computational imaging can be applied to close the gap, a novel full-color imaging system, adopting end-to-end joint optimization of metalens and CGH for high imaging quality, is proposed in this paper. The U-net based network as the pre-processing adjusts weights to make the holographic reconstruction offset imaging defects, incorporating the imaging processing into the step of generating hologram. Optimized by deep learning, the proposed imaging system is capable of full-color imaging with high fidelity in a compact form factor, envisioned to take an essential step towards the high-performance miniaturized imaging system.

Shortwave infrared single-pixel spectral imaging based on a GSST phase-change metasurface.

Shortwave infrared (SWIR) spectral imaging obtains spectral fingerprints corresponding to overtones of molecular vibrations invisible to conventional silicon-based imagers. However, SWIR imaging is challenged by the excessive cost of detectors. Single-pixel imaging based on compressive sensing can alleviate the problem but meanwhile presents new difficulties in spectral modulations, which are prerequisite in compressive sampling. In this work, we theoretically propose a SWIR single-pixel spectral imaging system with spectral modulations based on a Ge2Sb2Se4Te1 (GSST) phase-change metasurface. The transmittance spectra of the phase-change metasurface are tuned through wavelength shifts of multipole resonances by varying crystallinities of GSST, validated by the multipole decompositions and electromagnetic field distributions. The spectral modulations constituted by the transmittance spectra corresponding to the 11 phases of GSST are sufficient for the compressive sampling on the spectral domain of SWIR hyperspectral images, indicated by the reconstruction in false color and point spectra. Moreover, the feasibility of optimization on phase-change metasurface via coherence minimization is demonstrated through the designing of the GSST pillar height. The concept of spectral modulation with phase-change metasurface overcomes the static limitation in conventional modulators, whose integratable and reconfigurable features may pave the way for high-efficient, low-cost, and miniaturized computational imaging based on nanophotonics.

Compressive single-pixel hyperspectral imaging using RGB sensors.

Hyperspectral imaging that obtains the spatial-spectral information of a scene has been extensively applied in various fields but usually requires a complex and costly system. A single-pixel detector based hyperspectral system mitigates the complexity problem but simultaneously brings new difficulties on the spectral dispersion device. In this work, we propose a low-cost compressive single-pixel hyperspectral imaging system with RGB sensors. Based on the structured illumination single-pixel imaging configuration, the lens-free system directly captures data by the RGB sensors without dispersion in the spectral dimension. The reconstruction is performed with a pre-trained spatial-spectral dictionary, and the hyperspectral images are obtained through compressive sensing. In addition, the spatial patterns for the structured illumination and the dictionary for the sparse representation are optimized by coherence minimization, which further improve the reconstruction quality. In both spatial and spectral dimensions, the intrinsic sparse properties of the hyperspectral images are made full use of for high sampling efficiency and low reconstruction cost. This work may introduce opportunities for optimization of computational imaging systems and reconstruction algorithms towards high speed, high resolution, and low cost future.

Continuous-zoom bifocal metalens by mutual motion of cascaded bilayer metasurfaces in the visible.

Metalens, a subcategory of metasurfaces, has been widely investigated by virtue of its miniature and ultrathin characteristics as well as versatile functionalities. In this study, a tunable bifocal metalens with two continuous-zoom foci is proposed and numerically verified. This design utilizes two cascaded layers of metasurfaces, and different phase profiles for incidences of opposite helicities are imparted on each layer by the combination of geometric phase and propagation phase. When two layers of metasurfaces are actuated laterally, focal lengths of both foci are tuned continuously, with the difference of both focal lengths increasing or decreasing. Additionally, the zoom range for each focus can be designed at will, and the relative intensity of both foci can be modulated by altering the ellipticity of incidence, with the focusing efficiency of the bifocal metalens varying from 19.8% to 32.7% for numerical apertures in a range from 0.53 to 0.78. The proposed device is anticipated to find applications in multi-plane imaging, optical tomography technique, optical data storage, and so on.

Simultaneous coded aperture and dictionary optimization in compressive spectral imaging via coherence minimization.

Coded aperture snapshot spectral imaging (CASSI) reconstructs a hyperspectral image from several two-dimensional (2D) projections via compressive sensing. The reconstruction quality and the sampling efficiency of CASSI can be effectively improved by decreasing the coherence of the underlying sensing matrix. Efforts have been made to minimize the coherence with individual optimization on coded aperture or sparse basis. In this paper, a simultaneous optimization on the system projection and the over-complete dictionary is introduced to minimize the Frobenius norm coherence. The dual-disperser structure and the RGB image sensor are adopted for the lowest coherence in terms of system configuration. The coded aperture and the dictionary are optimized with genetic algorithm and gradient descent respectively, and simultaneous optimization is conducted iteratively. Low coherence of sensing matrix is acquired after the simultaneous optimization, with both reconstruction quality and sampling efficiency significantly improved. Compared to the non-optimized system and state-of-the-art systems with individually optimized coded aperture or dictionary, the simultaneous optimization promotes the peak signal-to-noise ratio by more than 5dB. The coherence minimization via simultaneous optimization on the system matrix and the sparse representation basis may open opportunities for further development of other compressive-sensing-based computational imaging systems.

Tunable beam splitter using bilayer geometric metasurfaces in the visible spectrum.

Metasurfaces have been widely investigated for their capabilities of manipulating wavefront versatilely and miniaturizing traditional optical elements into ultrathin devices. In this study, a nanoscale tunable beam splitter utilizing a bilayer of geometric metasurfaces in the visible spectrum is proposed and numerically examined. Inspired by the diffractive Alvarez lens and multilayer geometric metasurfaces, opposite quadratic phase distributions are imparted on both layers, and a varying linear phase gradient will arise through relatively lateral displacement between two layers, generating tunable angles of deflection. In addition, such geometric metasurfaces offer opposite directions of phase gradients for orthogonal circularly polarized incidences, leading to effective polarization beam splitting. Results prove that the splitting angles can be tuned precisely, and the energy split ratio can be effectively changed according to the ellipticity of the polarized incidence. This design could find significant applications in optical communication, measurement, display, and so on.

Light-field-depth-estimation network based on epipolar geometry and image segmentation.

In this paper, we propose a convolutional neural network based on epipolar geometry and image segmentation for light-field depth estimation. Epipolar geometry is utilized to estimate the initial disparity map. Multi-orientation epipolar images are selected as input data, and the convolutional blocks are adopted based on the disparity of different-direction epipolar images. Image segmentation is used to obtain the edge information of the central sub-aperture image. By concatenating the output of the two parts, an accurate depth map could be generated with fast speed. Our method achieves a high rank on most quality assessment metrics in the HCI 4D Light Field Benchmark and also shows effectiveness in estimating accurate depth on real-world light-field images.

Transient breathing dynamics during extinction of dissipative solitons in mode-locked fiber lasers.

The utilization of the dispersive Fourier transformation approach has enabled comprehensive observation of the birth process of dissipative solitons in fiber lasers. However, there is still a dearth of deep understanding regarding the extinction process of dissipative solitons. In this study, we have utilized a combination of experimental and numerical techniques to thoroughly examine the breathing dynamics of dissipative solitons during the extinction process in an Er-doped mode-locked fiber laser. The results demonstrate that the transient breathing dynamics have a substantial impact on the extinction stage of both steady-state and breathing-state dissipative solitons. The duration of transient breathing exhibits a high degree of sensitivity to variations in pump power. Numerical simulations are utilized to produce analogous breathing dynamics within the framework of a model that integrates equations characterizing the population inversion in a mode-locked laser. These results corroborate the role of Q-switching instability in the onset of breathing oscillations. Furthermore, these findings offer new possibilities for the advancement of various operational frameworks for ultrafast lasers.

Metalens Eyepiece for 3D Holographic Near-Eye Display.

Near-eye display (NED) systems for virtual reality (VR) and augmented reality (AR) have been rapidly developing; however, the widespread use of VR/AR devices is hindered by the bulky refractive and diffractive elements in the complicated optical system as well as the visual discomfort caused by excessive binocular parallax and accommodation-convergence conflict. To address these problems, an NED system combining a 5 mm diameter metalens eyepiece and a three-dimensional (3D), computer-generated holography (CGH) based on Fresnel diffraction is proposed in this paper. Metalenses have been extensively studied for their extraordinary capabilities at wavefront shaping at a subwavelength scale, their ultrathin compactness, and their significant advantages over conventional lenses. Thus, the introduction of the metalens eyepiece is likely to reduce the issue of bulkiness in NED systems. Furthermore, CGH has typically been regarded as the optimum solution for 3D displays to overcome limitations of binocular systems, since it can restore the whole light field of the target 3D scene. Experiments are carried out for this design, where a 5 mm diameter metalens eyepiece composed of silicon nitride anisotropic nanofins is fabricated with diffraction efficiency and field of view for a 532 nm incidence of 15.7% and 31°, respectively. Furthermore, a novel partitioned Fresnel diffraction and resample method is applied to simulate the wave propagations needed to produce the hologram, with the metalens capable of transforming the reconstructed 3D image into a virtual image for the NED. Our work combining metalens and CGH may pave the way for portable optical display devices in the future.

Multispectral camouflage for infrared, visible, lasers and microwave with radiative cooling.

Interminable surveillance and reconnaissance through various sophisticated multispectral detectors present threats to military equipment and manpower. However, a combination of detectors operating in different wavelength bands (from hundreds of nanometers to centimeters) and based on different principles raises challenges to the conventional single-band camouflage devices. In this paper, multispectral camouflage is demonstrated for the visible, mid-infrared (MIR, 3-5 and 8-14 µm), lasers (1.55 and 10.6 µm) and microwave (8-12 GHz) bands with simultaneous efficient radiative cooling in the non-atmospheric window (5-8 µm). The device for multispectral camouflage consists of a ZnS/Ge multilayer for wavelength selective emission and a Cu-ITO-Cu metasurface for microwave absorption. In comparison with conventional broadband low emittance material (Cr), the IR camouflage performance of this device manifests 8.4/5.9 °C reduction of inner/surface temperature, and 53.4/13.0% IR signal decrease in mid/long wavelength IR bands, at 2500 W ∙ m-2 input power density. Furthermore, we reveal that the natural convection in the atmosphere can be enhanced by radiation in the non-atmospheric window, which increases the total cooling power from 136 W ∙ m-2 to 252 W ∙ m-2 at 150 °C surface temperature. This work may introduce the opportunities for multispectral manipulation, infrared signal processing, thermal management, and energy-efficient applications.

Inverse design of an integrated-nanophotonics optical neural network.

Artificial neural networks have dramatically improved the performance of many machine-learning applications such as image recognition and natural language processing. However, the electronic hardware implementations of the above-mentioned tasks are facing performance ceiling because Moore's Law is slowing down. In this article, we propose an optical neural network architecture based on optical scattering units to implement deep learning tasks with fast speed, low power consumption and small footprint. The optical scattering units allow light to scatter back and forward within a small region and can be optimized through an inverse design method. The optical scattering units can implement high-precision stochastic matrix multiplication with mean squared error <10-4 and a mere 4 × 4 µm2 footprint. Furthermore, an optical neural network framework based on optical scattering units is constructed by introducing "Kernel Matrix", which can achieve 97.1% accuracy on the classic image classification dataset MNIST.