arXiv subjects

Benquan Wang

Publications and source records attributed to Benquan Wang.

4 recordsLinked to original sources

arXiv2026

Recovering topological information of light by topological learning

The evolution of modern-day communication networks towards optical solutions with enhanced capacity and robustness is driving interest in topological light waves, exploiting their stability against perturbations through a topological invariant, e.g., the skyrmion number. However, detecting the underlying topology remains a computationally intense process even under ideal conditions, becoming intractable after passing through strongly disordered channels, where the degradation into unrecognisable speckle appears to destroy the topology. Here, we propose and demonstrate a topology-enhanced artificial intelligence (AI) approach to recover and classify such apparently lost topological information by computationally leveraging topological invariants in the data across many length scales. By aligning the topological classification of information with the topology of light, our topology-enhanced learning protocol, termed TOPO$^{2}$, achieves highly efficient recognition of the topological states of light, even from speckle, without the need for any prior learning. Our approach outperforms benchmark tests against standard computational algorithms and has the benefit of requiring just a single intensity pattern as the input, facilitating single-shot operation. To demonstrate this, we leverage the skyrmion number as a robust data carrier of images through a disordered channel, using TOPO$^{2}$ to accurately reconstruct the transmitted images. This work synergises topological photonics and topological AI for unravelling hidden topological signatures in light, opening a pathway towards robust communications even in extreme disordered environments.

physics.optics↗

arXiv2026

FLYNN: Robust Neural Network for Robot Navigation using Fly Brain Topology

While deep learning models achieve state-of-the-art performance in complex tasks, they remain brittle when faced with new environments or sensory deprivation. In contrast, biological systems exhibit remarkable tolerance to these challenges. We address this vulnerability by developing a recurrent neural network (RNN) whose architecture is directly derived from the synaptic-resolution brain connectome of the fruit fly Drosophila melanogaster. We demonstrate the feasibility of training the fly connectome neural network (FLYNN) to perform vision-based navigation in MuJoCo, achieving performance comparable to modern hand-crafted networks of similar parameter counts. Crucially, FLYNN exhibits superior resistance to out-of-distribution (OOD) data and tolerance to sensory loss without further training. It remained functional even under total vision loss while hand-crafted networks largely failed, even when specifically trained with camera dropout. Principal Component Analysis (PCA) of the internal state of FLYNN suggests that it exhibits a particularly high degree of representational modularity, which might be related to its robustness. Our work provides a new direction for designing resilient artificial agents following the topology of biological brains.

cs.RO↗

arXiv2026

Alignment-Free Nanometric Optical Metrology Enabled by Structured Light

Advances in the semiconductor industry are driven by the development of increasingly compact devices featuring intricate etched geometries, the characterization of which essentially requires ultraprecise, label-free, and real-time metrology. However, non-destructive and alignment-free optical metrology of sub-wavelength structures with nanometric resolution remains a major challenge. Here, we demonstrate a novel single-shot, label-free, and alignment-free optical metrology approach for determining the 1D position of sub-wavelength nanostructures, achieving lambda/110 (7.2 nm) precision. The high precision benefits from utilizing structured illuminations of Laguerre-Gaussian (LG) or Hermite-Gaussian (HG) beams, and the AI analyzing method can retrieve the information when such structured light interacts with sub-wavelength objects. Instead of relying on phase singularities in superoscillatory microscopy, our approach leverages spatially distributed phase jumps in HG and LG beams interacting with the nanostructures, providing an alignment-robust solution to the challenges in optical metrology. Such an alignment-free, non-destructive, and high-precision metrology technique enables real-time machine vision, semiconductor inspection, and advanced manufacturing.

physics.optics↗

arXiv2023

Retrieving positions of closely packed sub-wavelength nanoparticles from their diffraction patterns

Distinguishing two objects or point sources located closer than the Rayleigh distance is impossible in conventional microscopy. Understandably, the task becomes increasingly harder with a growing number of particles placed in close proximity. It has been recently demonstrated that subwavelength nanoparticles in closely packed clusters can be counted by AI-enabled analysis of the diffraction patterns of coherent light scattered by the cluster. Here we show that deep learning analysis can determine the actual position of the nanoparticle in the cluster of subwavelength particles from a sing-shot diffraction pattern even if they are separated by distances below the Rayleigh resolution limit of a conventional microscope.

physics.optics↗