Inverse design of compact nonvolatile reconfigurable silicon photonic devices with phase-change materials.

In the development of silicon photonics, the continued downsizing of photonic integrated circuits will further increase the integration density, which augments the functionality of photonic chips. Compared with the traditional design method, inverse design presents a novel approach for achieving compact photonic devices. However, achieving compact, reconfigurable photonic devices with the inverse design that employs the traditional modulation method exemplified by the thermo-optic effect poses a significant challenge due to the weak modulation capability.

Holographic multiplexing metasurface with twisted diffractive neural network.

As the cornerstone of AI generated content, data drives human-machine interaction and is essential for developing sophisticated deep learning agents. Nevertheless, the associated data storage poses a formidable challenge from conventional energy-intensive planar storage, high maintenance cost, and the susceptibility to electromagnetic interference. In this work, we introduce the concept of metasurface disk, meta-disk, to expand the capacity limits of optical holographic storage by leveraging uncorrelated structural twist.

Negative Conductivity Induced Reconfigurable Gain Metasurfaces and Their Nonlinearity.

The past decades have witnessed the rapid development of metamaterials and metasurfaces. However, loss is still a challenging problem limiting numerous practical applications, including long-range wireless communications, superscattering, and non-Hermitian physics. Recently, great effort has been made to minimize the loss, however, they are too complicated for practical implementation and still restricted by the theoretical limit.

Non-Invasive Self-Adaptive Information States' Acquisition inside Dynamic Scattering Spaces.

Pushing the information states' acquisition efficiency has been a long-held goal to reach the measurement precision limit inside scattering spaces. Recent studies have indicated that maximal information states can be attained through engineered modes; however, partial intrusion is generally required. While non-invasive designs have been substantially explored across diverse physical scenarios, the non-invasive acquisition of information states inside dynamic scattering spaces remains challenging due to the intractable non-unique mapping problem, particularly in the context of multi-target scenarios.

3D Intelligent Cloaked Vehicle Equipped with Thousand-Level Reconfigurable Full-Polarization Metasurfaces.

A crucial aspect in shielding a variety of advanced electronic devices from electromagnetic detection involves controlling the flow of electromagnetic waves, akin to invisibility cloaks. Decades ago, the exploration of transformation optics heralded the dawn of modern invisibility cloaks, which has stimulated immense interest across various physical scenarios. However, most prior research is simplified to low-dimensional and stationary hidden objects, limiting their practical applicability in a dynamically changing world.

Polarization-Orthogonal Nondegenerate Plasmonic Higher-Order Topological States.

Photonic topological states, providing light-manipulation approaches in robust manners, have attracted intense attention. Connecting photonic topological states with far-field degrees of freedom (d.o.

Eye accommodation-inspired neuro-metasurface focusing.

The human eye, which relies on a flexible and controllable lens to focus light onto the retina, has inspired many scientific researchers to understand better and imitate the biological vision system. However, real-time environmental adaptability presents an enormous challenge for artificial eye-like focusing systems. Inspired by the mechanism of eye accommodation, we propose a supervised-evolving learning algorithm and design a neuro-metasurface focusing system.

MagTetris: A simulator for fast magnetic field and force calculation for permanent magnet array designs.

In this paper, a simulator named "MagTetris" is proposed for fast magnetic field (B-field) and force calculation for permanent magnet arrays (PMAs) designs consisting of cuboid and arc-shaped magnets (approximated by cuboids) with arbitrary configurations. The proposed simulator can compute the B-field of a PMA on arbitrary observation planes and the magnetic force acting on any magnet/group of magnets. An accelerated calculation method for B-fields of PMAs is developed based on the current model of permanent magnet, which is further extended to magnetic force calculation.

Inverse design of a photonic moiré lattice waveguide towards improved slow light performances.

Slow light waveguides in photonic crystals are engineered using a conventional method or a deep learning (DL) method, which is data-intensive and suffers from data inconsistency, and both methods result in overlong computation time with low efficiency. In this paper, we overcome these problems by inversely optimizing the dispersion band of a photonic moiré lattice waveguide using automatic differentiation (AD). The AD framework allows the creation of a definite target band to which a selected band is optimized, and a mean square error (MSE) as an objective function between the selected and the target bands is used to efficiently compute gradients using the autograd backend of the AD library.

A knowledge-inherited learning for intelligent metasurface design and assembly.

Recent breakthroughs in deep learning have ushered in an essential tool for optics and photonics, recurring in various applications of material design, system optimization, and automation control. Deep learning-enabled on-demand metasurface design has been the subject of extensive expansion, as it can alleviate the time-consuming, low-efficiency, and experience-orientated shortcomings in conventional numerical simulations and physics-based methods. However, collecting samples and training neural networks are fundamentally confined to predefined individual metamaterials and tend to fail for large problem sizes.

Broadband Flexible Microstrip Antenna Array with Conformal Load-Bearing Structure.

To enhance the load-bearing mechanical properties and broadband electromagnetic characteristics of the conformal antenna, a broadband microstrip antenna array with a conformal load-bearing structure is proposed in this paper, which consists of three flexible substrate layers and two honeycomb core layers stacked on each other. By combining the antenna and honeycomb core layer in a structural perspective, the antenna array is implemented in the composition function of surface conformability and load-bearing. Additionally, the sidelobe level of the antenna is suppressed based on the reflection surface loaded.

STSC-SNN: Spatio-Temporal Synaptic Connection with temporal convolution and attention for spiking neural networks.

Spiking neural networks (SNNs), as one of the algorithmic models in neuromorphic computing, have gained a great deal of research attention owing to temporal information processing capability, low power consumption, and high biological plausibility. The potential to efficiently extract spatio-temporal features makes it suitable for processing event streams. However, existing synaptic structures in SNNs are almost full-connections or spatial 2D convolution, neither of which can extract temporal dependencies adequately.

Radiative anti-parity-time plasmonics.

Space and guided electromagnetic waves, as widely known, are two crucial cornerstones in extensive wireless and integrated applications respectively. To harness the two cornerstones, radiative and integrated devices are usually developed in parallel based on the same physical principles. An emerging mechanism, i.

High-performance permanent magnet array design by a fast genetic algorithm (GA)-based optimization for low-field portable MRI.

Lightweight and compact permanent magnet arrays (PMAs) are suitable for portable dedicated magnetic resonance imaging (MRI). It is worth exploring different PMA design possibilities and optimization methods with an adequate balance between weight, size, and performance, in addition to Halbach arrays and C-shaped/H-shaped magnets which are widely used. In this paper, the design and optimization of a sparse high-performance inward-outward ring-pair PMA consisting of magnet cuboids is presented for portable imaging of the brain.

Photonic Moiré lattice waveguide with a large slow light bandwidth and delay-bandwidth product.

We proposed an effective approach to enlarge the slow light bandwidth and normalized-delay-bandwidth product in an optimized moiré lattice-based photonic crystal waveguide that exhibits intrinsic mid-band characteristics. A flatband corresponding to a nearly constant group index of 34 over a wide bandwidth of 82 nm centered at 1550 nm with near-zero group velocity dispersion was achieved. A large normalized-delay-bandwidth product of 0.

MAP-SNN: Mapping spike activities with multiplicity, adaptability, and plasticity into bio-plausible spiking neural networks.

Spiking Neural Networks (SNNs) are considered more biologically realistic and power-efficient as they imitate the fundamental mechanism of the human brain. Backpropagation (BP) based SNN learning algorithms that utilize deep learning frameworks have achieved good performance. However, those BP-based algorithms partially ignore bio-interpretability.

Experimental Realization of a Superdispersion-Enabled Ultrabroadband Terahertz Cloak.

Invisibility has been a topic of long-standing interest owing to the advent of metamaterials and transformation optics, but still faces open challenges after its tremendous development in recent decades. One of the big challenges is the narrow bandwidth, as the realization of an invisibility cloak is usually based on a metamaterial-an artificial composite material composed of subwavelength resonator structures that are always associated with dispersion. Different from previous works that have tried to eliminate the material dispersion to enhance the bandwidth of an invisibility cloak, here, it is found that by judiciously harnessing the material dispersion, the bandwidth of the cloak can still be significantly increased.

Breaking the fundamental scattering limit with gain metasurfaces.

A long-held tenet in physics asserts that particles interacting with light suffer from a fundamental limit to their scattering cross section, referred to as the single-channel scattering limit. This notion, appearing in all one, two, and three dimensions, severely limits the interaction strength between all types of passive resonators and photonic environments and thus constrains a plethora of applications in bioimaging, sensing, and photovoltaics. Here, we propose a route to overcome this limit by exploiting gain media.

Homeostatic neuro-metasurfaces for dynamic wireless channel management.

The physical basis of a smart city, the wireless channel, plays an important role in coordinating functions across a variety of systems and disordered environments, with numerous applications in wireless communication. However, conventional wireless channel typically necessitates high-complexity and energy-consuming hardware, and it is hindered by lengthy and iterative optimization strategies. Here, we introduce the concept of homeostatic neuro-metasurfaces to automatically and monolithically manage wireless channel in dynamics.

Dynamic recognition and mirage using neuro-metamaterials.

Breakthroughs in the field of object recognition facilitate ubiquitous applications in the modern world, ranging from security and surveillance equipment to accessibility devices for the visually impaired. Recently-emerged optical computing provides a fundamentally new computing modality to accelerate its solution with photons; however, it still necessitates digital processing for in situ application, inextricably tied to Moore's law. Here, from an entirely optical perspective, we introduce the concept of neuro-metamaterials that can be applied to realize a dynamic object- recognition system.

First-principles investigation of copper diffusion barrier performance in defective 2D layered materials.

This paper investigates the diffusion barrier performance of 2D layered materials with pre-existing vacancy defects using first-principles density functional theory. Vacancy defects in 2D materials may give rise to a large amount of Cu accumulation, and consequently, the defect becomes a diffusion path for Cu. Five 2D layered structures are investigated as diffusion barriers for Cu, i.

Enhanced Photodetection Performance of Photodetectors Based on Indium-Doped Tin Disulfide Few Layers.

Two dimensional (2D) tin disulfide (SnS) has attracted growing interest as a promising high performance photodetector with superior performance such as fast response time, high responsivity, and good stability. However, SnS-based photodetectors still face great challenges, and the photodetection performance needs to be improved for practical applications. Herein, indium-doped SnS (In-SnS) few layers were exfoliated from CVT-grown single crystals, which were synthesized by chemical vapor transport.

Long-term stability of arsenic calcium residue (ACR) treated with FeSO and HSO: Function of H and Fe(Ⅱ).

Arsenic calcium residue (ACR) generated from the As-bearing wastewater treatment is highly hazardous due to high content of available As, which was seeking a suitable method for safe disposal such as stabilization treatment. In this study, the stabilization of available As in ACR was performed by combined treatment with FeSO and HSO. After stabilization treatment, the As leaching concentrations extracted by China Standard Leaching Test (CSLT, HJ/T299-2007) decreased significantly from 162 mg/L to less than the Chinese regulation limit of 1.

Band characterization of topological photonic crystals using the broadband Green's function technique.

A novel method is developed in this paper to characterize the band diagram and band modal fields of gyromagnetic photonic crystals that support topological one-way edge states. The proposed method is based on an integral equation formulation that utilizes the broadband Green's function (BBGF). The BBGF is a hybrid representation of the periodic lattice Green's function with imaginary extractions that has accelerated convergence and is suitable for broadband evaluations.