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<p>An integrated plasmonic neurosensing platform is introduced to enable ultrafast, self-learning anomaly detection within next-generation Internet of Things (IoT) environments. The research attempts to design an all-optical plasmonic neurosensor that can monitor irregularities as well as at the same time learns in hardware without the aid of electronics. The big picture is to develop an ultra-fast energy-saving sensorial unit that can scale to large tissues of IoT network applications and, autonomously, adjusts to varying conditions. The most significant invention of the paper is that localized surface plasmon resonance (LSPR) nanostructures are proposed to combine both nonlinear optical memory-effect and physical learning in sensor plasmonic gap. The technique is a hybrid between FDTD/FEM electromagnetic modelling, nanoimprint based production of sub-20-nm bow-tie antennas, nonlinear optical modulation experimental studies, and scalability analysis on the network level. A simulated system determined the optimal bow-tie configuration that resonated at 817&#xa0;nm with a field enhancement of approximately 28x with gap dimensions of 10&#xa0;nm long. Fabricated devices attained resonance of 823&#xa0;nm with Q-factor of 18.7. A refractive-index modulation was achieved of 3.1 × 10⁻³ and overall shift of the resonance at 51&#xa0;nm of 50 cycles in optical learning. The IoT level testing had 94.6% anomaly-detection errors and 47 ps response time, whereas the scalability experiment enabled the growth of bandwidth linearly with WDM and 92% fabrication yield. These findings provide an answer to the consequences that will lead to ultra-dense self-learning photonic IoT designs.</p>

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All-Optical Plasmonic Neurosensor for Self-Learning Anomaly Detection in Smart IoT Systems

  • 期刊论文

  • 发表日期：

    2026年1月21日

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  • GV Shrichandran,
  • Gayathry S. Warrier,
  • K Vignesh,
  • A. Rajendran,
  • Shamimul Qamar,
  • Mohammed Shuaib,
  • A. Rajaram

摘要

An integrated plasmonic neurosensing platform is introduced to enable ultrafast, self-learning anomaly detection within next-generation Internet of Things (IoT) environments. The research attempts to design an all-optical plasmonic neurosensor that can monitor irregularities as well as at the same time learns in hardware without the aid of electronics. The big picture is to develop an ultra-fast energy-saving sensorial unit that can scale to large tissues of IoT network applications and, autonomously, adjusts to varying conditions. The most significant invention of the paper is that localized surface plasmon resonance (LSPR) nanostructures are proposed to combine both nonlinear optical memory-effect and physical learning in sensor plasmonic gap. The technique is a hybrid between FDTD/FEM electromagnetic modelling, nanoimprint based production of sub-20-nm bow-tie antennas, nonlinear optical modulation experimental studies, and scalability analysis on the network level. A simulated system determined the optimal bow-tie configuration that resonated at 817 nm with a field enhancement of approximately 28x with gap dimensions of 10 nm long. Fabricated devices attained resonance of 823 nm with Q-factor of 18.7. A refractive-index modulation was achieved of 3.1 × 10⁻³ and overall shift of the resonance at 51 nm of 50 cycles in optical learning. The IoT level testing had 94.6% anomaly-detection errors and 47 ps response time, whereas the scalability experiment enabled the growth of bandwidth linearly with WDM and 92% fabrication yield. These findings provide an answer to the consequences that will lead to ultra-dense self-learning photonic IoT designs.