Reconfigurable, programmable and compact devices hold immense importance in high-density programmable photonic-integrated circuits (PICs) used in optical communications and signal processing. Compared with traditional methods of designing devices, the inverse design approach provides a new avenue for achieving high-density PICs. However, the majority of programmable photonic systems result in large footprints and high energy consumption due to the relatively weak modulation capability. This severely limits high-density integration of on-chip photonic systems. In recent years, chalcogenide phase-change materials (PCMs) have emerged as a promising alternative for achieving reconfigurable photonic devices by utilizing their high refractive index contrast and reversible phase transition between amorphous and crystalline states. In this work, we demonstrate a 1 × 2 multifunctional and compact photonic device based on phase-change material Sb2Se3 with footprints of only 4.8 × 4.8 μm2. We adopt the adjoint optimization approach to design the Sb2Se3 film on the silicon 3dB power splitter designed in advance. Subsequently, we can use laser heating technique to change the phase patterns on the Sb2Se3 film to achieve the designed device function. This enables the device to be reconfigurable from an optical switch to an arbitrary ratio power splitter. Remarkably, the functionality of our photonic device is rewritable, allowing for the erasure and reconfiguration of the Sb2Se3phase pattern. The demonstrated programmable phase-change photonic devices will significantly enhance the flexibility of photonic devices and promote the development of high-density programmable PICs.
Dielectric laser accelerators (DLAs) can achieve acceleration gradients that are 1 to 2 orders of magnitude higher than traditional radiofrequency (RF) accelerators. Due to the micrometer-scale dimensions of the DLA acceleration structure, on the one hand, high requirements are imposed on the quality of the electron beam and the alignment among the electron beam, driving laser, and DLA in experiments. On the other hand, although the DLA acceleration gradient is very high, the acceleration distance is insufficient, resulting in relatively small absolute energy gain for sub-relativistic electrons after DLA acceleration. These electrons are susceptible to interference from stray electromagnetic fields during propagation, posing significant challenges to the resolution and accuracy of sub-relativistic electron energy spectrum testing. Based on the principle of laser-driven grating-structure DLA for accelerating electron beams, this paper designs a complete test system, constructs electronic dynamics simulation models and magnetic field measurement electron energy spectrum simulation models for verification, designs a double-layer magnetic shunt to shield the interference of leakage magnetic fields on electrons in the simulation, considers factors such as the beam spot radius, divergence angle, and geomagnetic field intensity of the electron beam that conform to experimental conditions, and finally obtains simulated images of electron spots on the fluorescent screen after the electron beam is deflected by the magnetic field. The electron dynamics simulation results show that the electron beam achieves a maximum energy gain of 14.4401 keV over an acceleration length of 36 μm, with an acceleration gradient reaching 401.114 MeV/m. Based on the acceleration effect, a magnetic field of 𝐵𝐵 = 170 Gs is set, and an electron spot after deflection is obtained. The edges of the spots before and after acceleration are separated by Δ𝑑 = 4.2889 mm, and the maximum energy gain measurement error is εΔΕ' = 4.45%, realizing precise measurement of the energy gain of sub-relativistic electrons.
Active nanophotonic devices play an important role in optical communication systems. Improving modulation efficiency and reducing the footprint of these devices are crucial for low energy information processing and on chip communication. The Multi Frequency-Domain Finite Difference (MF-FDFD) algorithm was invented to address the inherent the large difference in scale between the modulation frequency and the frequency of optical waves of active nanophotonic devices. However, due to the fact that the MF-FDFD algorithm itself requires solving a large number of unknowns compared to traditional FDFD algorithms, the current MF-FDFD algorithm still stays in two-dimensional and is difficult to calculate in three-dimensional. To solve this problem, we introduced Newton and Born methods to accelerate and complete the the solution of algorithm, and developed a three-dimensional MF-FDFD algorithm. We validated our algorithm by simulating a mode converter. Our algorithm can effectively perform first principles simulations for active nanophotonic devices, laying the foundation for future intelligent inverse design.
The finite-difference frequency-domain (FDFD) method is an effective method for numerical simulation of electromagnetic fields. It has great advantages in dealing with electromagnetic scattering problems of complex structures and complex media. This method can transform the frequency-domain Maxwell equations into a linear system for solution by difference operation on the spatial grid. However, high-precision differential calculations can result in more memory consumption and a decrease in computational speed. In previous reports, subgridding technique is often used to solve such problems, where mesh refinement is only performed in local areas, while coarse mesh partitioning is still used in other areas. However, the refinement area can only be manually set, lacking flexibility and accuracy. Therefore, we propose a novel FDFD method based on adaptive grids, which uses the cartesian tree-based hierarchical grids to discrete the spatial domain. It can automatically refine the local grids according to the geometrical characteristic of the model to improve the accuracy of specific areas, without significantly increasing the number of unknowns, and has strong flexibility while improving the calculation efficiency. In this study, we use two levels of grids for adaptive grids construction, with a mesh size ratio of 3:1. Using second-order interpolation to handle the transmission problem of electromagnetic field components at different grid boundaries. The simulation results show that the computation speed of the adaptive grids FDFD system is faster than that of structured grids.
The object tracking accuracy may be decreased because of the camera jitter, making it extremely hard for object tracking and trajectory analyzation. To achieve accurate video stabilization, the movement of camera can be analyzed and predicted based on the previous camera jitter sequence. In the area of sequence prediction, the long-short term memory (LSTM) network shows the potential in sequence forecasting, here we use LSTM network in camera jitter prediction and video stabilization. In this paper, we propose a video stabilization algorithm based on multi-region grey projection method and LSTM encoder-decoder network. Our algorithm calculates the motion of the camera through the gray projection of four areas in each frame, then filters out the main movement direction and jitter of the camera. The LSTM encoder-decoder network receives the camera jitter sequence, predicts the camera jitter then stabilizes the video. We to verify the performance of the proposed video stabilization method. We tested the proposed video stabilization algorithm on the jitter videos, which is made by the VisDrone dataset video modified with our recorded camera jitter. Experimental results demonstrate that the proposed method can achieve the video stabilization in real time, and increase the accuracy of object tracking and trajectory analyzation.
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