Shape reconstructions by using plasmon resonances with enhanced sensitivity

TL;DR

This paper presents a novel shape reconstruction method for sub-wavelength objects using plasmon resonances, significantly improving sensitivity and robustness in near-field electromagnetic scattering measurements.

Contribution

The paper introduces a new reconstruction scheme leveraging plasmon resonances, spectral analysis, and variational regularization to enhance sensitivity and address ill-posedness.

Findings

Sensitivity blows up at plasmon resonance, improving SNR

The method achieves robust reconstructions with theoretical validation

Numerical experiments confirm effectiveness and accuracy

Abstract

This paper investigates the shape reconstructions of sub-wavelength objects from near-field measurements in transverse electromagnetic scattering. This geometric inverse problem is notoriously ill-posed and challenging. We develop a novel reconstruction scheme using plasmon resonances with significantly enhanced sensitivity and resolution. First, by spectral analysis, we establish a sharp quantitative relationship between the sensitivity of the reconstruction and the plasmon resonance. It shows that the sensitivity functional blows up when plasmon resonance occurs. Hence, the signal-to-noise ratio is significantly improved and the robustness and effectiveness of the reconstruction are ensured. Second, a variational regularization method is proposed to overcome the ill-posedness, and an alternating iteration method is introduced to automatically select the regularization parameters. Third, we use the Laplace approximation method to capture the statistical information of the target scattering object. Both rigorous theoretical analysis and extensive numerical experiments are conducted to validate the promising features of our method.