Infrared Lightwave Memory-Resident Manipulation and Absorption Based on Spatial Electromagnetic Wavefield Excitation and Resonant Accumulation by GdFe-Based Nanocavity-Shaped Metasurfaces.

An arrayed nanocavity-shaped architecture consisting of the key GdFe film and SiO dielectric layer is constructed, leading to an efficient infrared (IR) absorption metasurface. By carefully designing and optimizing the film system configuration and the surface layout with needed geometry, a desirable IR radiation absorption according to the spatial magnetic plasmon modes is realized experimentally. The simulations and measurements demonstrate that GdFe-based nanocavity-shaped metasurfaces can be used to achieve an average IR absorption of ~81% in a wide wavelength range of 3-14 μm.

Research on the localized lightfield features of metallic nano-cone-tip optical antenna via investigating near-field lightwave and correlated net-charge distribution.

In this paper, the near-field lightwave characteristics of an arrayed silicon nano-cone-tip optical antenna (NOA) covered by a common metal film, which can be viewed as a featured quasi quantum dot (QD), are carefully investigated. A dipole net-charge distribution closely correlated with the surface lightwaves excited over the antennas by incident lasers with a central wavelength of 633 nm, is clearly observed. An obvious Coulomb-like blockade from the apex apparently influencing the net-charge converging over the surface of NOA, is verified, which can also be predicted by the simulations according to surface standing waves across the apex node.

The -MDA: An Invariant to Shifting, Scaling, and Rotating Variance for 3D Object Recognition Using Diffractive Deep Neural Network.

The diffractive deep neural network (DNN) can efficiently accomplish 2D object recognition based on rapid optical manipulation. Moreover, the multiple-view DNN array (MDA) possesses the obvious advantage of being able to effectively achieve 3D object classification. At present, 3D target recognition should be performed in a high-speed and dynamic way.

Electrically addressed focal stack plenoptic camera based on a liquid-crystal microlens array for all-in-focus imaging.

Focal stack cameras are capable of capturing a stack of images focused at different spatial distance, which can be further integrated to present a depth of field (DoF) effect beyond the range restriction of conventional camera's optics. To date, all of the proposed focal stack cameras are essentially 2D imaging architecture to shape 2D focal stacks with several selected focal lengths corresponding to limited objective distance range. In this paper, a new type of electrically addressed focal stack plenoptic camera (EAFSPC) based on a functional liquid-crystal microlens array for all-in-focus imaging is proposed.

Orthogonal separation of arbitrary vector beams from non-polarized light waves based on a patterned liquid-crystal photo-alignment.

An effective method for orthogonally separating arbitrary vector polarized beams from non-polarized incident light waves is proposed in this Letter. A tunable patterned spatial distribution of liquid-crystal (LC) molecules can be effectively constructed based on both the initial photo-alignment and the electrically controlled birefringence of nematic LC materials. The LC photo-alignment over a smooth surface without any common nano-grooves leads to a highly efficient light-wave transformation by inducing a desired initial arrangement of LC directors and then acquiring extraordinary light waves with the needed, or even arbitrary, spatial polarization.

Lightwave nano-converging enhancement by an arrayed optical antenna based on metallic nano-cone-tips for CMOS imaging detection.

A kind of gold-coated glass nano-cone-tips (GGNCTs) is developed as an arrayed optical antenna for highly receiving and converging incident lightwaves. A local light field enhancement factor (LFEF) of ~ 2 × 10 and maximum light absorption of ~ 98% can be achieved. The near-field lightwave measurements at the wavelength of 633 nm show that the surface net charges over a single GGNCT make a typical dipole oscillation and the energy transmits along the wave vector orientation, thus leading to a strong local light field enhancement.

Spatial separation of azimuthally and radially polarized beams from non-polarized light waves based on the electrically controlled birefringence effect.

Based on the electrically controlled birefringence effect in liquid crystal materials, an effective method for spatially separating azimuthally and radially polarized beams from non-polarized incident light waves is proposed. The radially polarized beam was highly converged by using a microhole-patterned electrode and a planar photo-alignment layer to shape the initial liquid-crystal radial alignment and a gradient refractive index distribution with central axial symmetry after applying a voltage signal. Due to the intrinsic polarization sensitivity of nematic liquid-crystal materials, the shaped gradient refractive index only applies to extraordinary light waves, which then converge into a spot.

All-In-Focus Polarimetric Imaging Based on an Integrated Plenoptic Camera with a Key Electrically Tunable LC Device.

In this paper, a prototyped plenoptic camera based on a key electrically tunable liquid-crystal (LC) device for all-in-focus polarimetric imaging is proposed. By using computer numerical control machining and 3D printing, the proposed imaging architecture can be integrated into a hand-held prototyped plenoptic camera so as to greatly improve the applicability for outdoor imaging measurements. Compared with previous square-period liquid-crystal microlens arrays (LCMLA), the utilized hexagonal-period LCMLA has remarkably increased the light utilization rate by ~15%.

Broad-spectrum diffractive network via ensemble learning.

We propose a broad-spectrum diffractive deep neural network (BS-DNN) framework, which incorporates multiwavelength channels of input lightfields and performs a parallel phase-only modulation using a layered passive mask architecture. A complementary multichannel base learner cluster is formed in a homogeneous ensemble framework based on the diffractive dispersion during lightwave modulation. In addition, both the optical sum operation and the hybrid (optical-electronic) maxout operation are performed for motivating the BS-DNN to learn and construct a mapping between input lightfields and truth labels under heterochromatic ambient lighting.

Multiple-view DNNs array: realizing robust 3D object recognition.

As an optical-based classifier of the physical neural network, the independent diffractive deep neural network () can be utilized to learn the single-view spatial featured mapping between the input lightfields and the truth labels by preprocessing a large number of training samples. However, it is still not enough to approach or even reach a satisfactory classification accuracy on three-dimensional (3D) targets owing to already losing lots of effective lightfield information on other view fields. This Letter presents a multiple-view array (MDA) scheme that provides a significant inference improvement compared with individual or Res- by constructing a different complementary mechanism and then merging all base learners of distinct views on an electronic computer.

Robust light beam diffractive shaping based on a kind of compact all-optical neural network.

A kind of compact all-optical learning-based neural network has been constructed and characterized for efficiently performing a robust layered diffractive shaping of laser beams. The data-driven control lightwave strategy demonstrates some particular advantages such as smart or intelligent light beam manipulation, optical data statistical inference and incident beam generalization. Based on the proposed method, several typical aberrated light fields can be effectively modulated into the desired fashion including the featured flat-top beams, an arrayed sub-beam arrangement and complex annular fringes compared with conventional GS-based DOEs.

Anti-noise diffractive neural network for constructing an intelligent imaging detector array.

To develop an intelligent imaging detector array, a diffractive neural network with strong robustness based on the Weight-Noise-Injection training is proposed. According to layered diffractive transformation under existing several errors, an accurate and fast object classification can be achieved. The fact that the mapping between the input image and the label in Weight-Noise-Injection training mode can be learned, means that the prediction of the optical network being insensitive to disturbances so as to improve its noise resistance remarkably.

Electrically Controlled Liquid Crystal Microlens Array Based on Single-Crystal Graphene Coupling Alignment for Plenoptic Imaging.

As a unique electric-optics material, liquid crystals (LCs) have been used in various light-control applications. In LC-based light-control devices, the structural alignment of LC molecules is of great significance. Generally, additional alignment layers are required for LC lens and microlens, such as rubbed polyimide (PI) layers or photoalignment layers.

Generating Convergent Laguerre-Gaussian Beams Based on an Arrayed Convex Spiral Phaser Fabricated by 3D Printing.

A convex spiral phaser array (CSPA) is designed and fabricated to generate typical convergent Laguerre-Gaussian (LG) beams. A type of 3D printing technology based on the two-photon absorption effect is used to make the CSPAs with different featured sizes, which present a structural integrity and fabricating accuracy of ~200 nm according to the surface topography measurements. The light field vortex characteristics of the CSPAs are evaluated through illuminating them by lasers with different central wavelength such as 450 nm, 530 nm and 650 nm.

Depth-of-Field-Extended Plenoptic Camera Based on Tunable Multi-Focus Liquid-Crystal Microlens Array.

Plenoptic cameras have received a wide range of research interest because it can record the 4D plenoptic function or radiance including the radiation power and ray direction. One of its important applications is digital refocusing, which can obtain 2D images focused at different depths. To achieve digital refocusing in a wide range, a large depth of field (DOF) is needed, but there are fundamental optical limitations to this.

Label enhanced and patch based deep learning for phase retrieval from single frame fringe pattern in fringe projection 3D measurement.

We propose a label enhanced and patch based deep learning phase retrieval approach which can achieve fast and accurate phase retrieval using only several fringe patterns as training dataset. To the best of our knowledge, it is the first time that the advantages of the label enhancement and patch strategy for deep learning based phase retrieval are demonstrated in fringe projection. In the proposed method, the enhanced labeled data in training dataset is designed to learn the mapping between the input fringe pattern and the output enhanced fringe part of the deep neural network (DNN).