arXiv subjects

Chengjun Zou

Publications and source records attributed to Chengjun Zou.

3 recordsLinked to original sources

arXiv2026

Sensitive biodetection in flow using metasurface hosting quasi-bound state in the continuum resonances

We have designed optical metasurfaces hosting high-quality factor quasi-bound state in the continuum (q-BIC) resonances for optical biosensing in flow. The unit cell of the metasurface contains two rectangular bars. An asymmetry factor is introduced by varying the gap width between the bars, to enable optical coupling to a q-BIC resonance confined to the air gap between neighboring nanoresonators. The location of the resonances makes them highly sensitive to changes in the local refractive index, leading to experimental bulk refractive index sensitivities exceeding 315 +/- 22 nm/RIU and a figure-of-merit of 66 +/- 5 RIU-1. Successful streptavidin-biotin binding was observed by measuring the metasurface transmission in real-time by exposing the metasurface to various concentrations of analytes via a commercial microfluidic flow cell apparatus. The experimental limit of detection, defined as 3{\sigma} above noise, was found to be 1.8x10-8 M. This platform represents a compact optical approach for point-of-care diagnostics with fast read-out.

physics.optics↗

arXiv2026

Target-depth sensing with metasurface-encoder integrated optoelectronic neural network

Accurate and real-time sensing of targets in three-dimensional (3D) environments is essential for modern machine vision, underpinning emerging technologies such as autonomous systems, robotic manipulation, augmented reality, and intelligent surveillance. However, state-of-the-art 3D sensing approaches typically rely on complex postprocessing of multi-view images or LiDAR point clouds, resulting in considerable computational load, power consumption, and latency. To address these challenges, we propose a metasurface-encoder integrated optoelectronic neural network architecture that compresses 3D information into two-dimensional images by encoding depth using double-helix point spread function generated by a metasurface. The depth-encoded images are captured with a conventional monocular camera and subsequently processed by a lightweight shadow ResNet neural network. We experimentally validate the proposed architecture on the MNIST and Vehicle-Image datasets, achieving high accuracy simultaneously in target classification and depth estimation, thereby enabling real-time target tracking. The framework is readily extendable to other depth- or angle-encoding metasurfaces for multidimensional compression and detection. Our results demonstrate the effectiveness of the meta-optic-encoder/electronic-decoder paradigm in significantly reducing network complexity and computational burden while maintaining strong performance for smart vision sensory applications.

physics.optics↗

arXiv2025

Metasurface-based all-optical diffractive convolutional neural networks

The escalating energy demands and parallel-processing bottlenecks of electronic neural networks underscore the need for alternative computing paradigms. Optical neural networks, capitalizing on the inherent parallelism and speed of light propagation, present a compelling solution. Nevertheless, physically realizing convolutional neural network (CNN) components all-optically remains a significant challenge. To this end, we propose a metasurface-based all-optical diffractive convolutional neural network (MAODCNN) for computer vision tasks. This architecture synergistically integrates metasurface-based optical convolutional layers, which perform parallel convolution on the optical field, with cascaded diffractive neural networks acting as all-optical decoders. This co-design facilitates layer-wise feature extraction and optimization directly within the optical domain. Numerical simulations confirm that the fusion of convolutional and diffractive layers markedly enhances classification accuracy, a performance that scales with the number of diffractive layers. The MAODCNN framework establishes a viable foundation for practical all-optical CNNs, paving the way for high-efficiency, low-power optical computing in advanced pattern recognition.

physics.optics↗