Machine Learning-Enabled Multidimensional Data Utilization Through Multi-Resonance Architecture: A Pathway to Enhanced Accuracy in Biosensing

TL;DR

This paper introduces a machine learning framework that utilizes multi-resonance biosensors with a novel nanostructure to significantly improve the accuracy of biosensing measurements without hardware changes.

Contribution

It presents a new multi-resonance nanostructure combined with ML analysis, achieving up to three orders of magnitude improvement in detection precision over traditional single-resonance systems.

Findings

Multi-resonance structure achieves high sensitivity up to 1706 nm/RIU.

ML analysis with multiple resonances greatly enhances detection precision.

The approach improves biosensing accuracy without hardware modifications.

Abstract

A novel framework is proposed that combines multi-resonance biosensors with machine learning (ML) to significantly enhance the accuracy of parameter prediction in biosensing. Unlike traditional single-resonance systems, which are limited to one-dimensional datasets, this approach leverages multi-dimensional data generated by a custom-designed nanostructure, a periodic array of silicon nanorods with a triangular cross-section over an aluminum reflector. High bulk sensitivity values are achieved for this multi-resonant structure, with certain resonant peaks reaching up to 1706 nm/RIU. The field analysis reveals Mie resonances as the physical reason behind the peaks. The predictive power of multiple resonant peaks from transverse magnetic (TM) and transverse electric (TE) polarizations is evaluated using Ridge Regression modeling. Systematic analysis reveals that incorporating multiple resonances yields up to three orders of magnitude improvement in refractive index detection precision compared to single-peak analyses. This precision enhancement is achieved without modifications to the biosensor hardware, highlighting the potential of data-centric strategies in biosensing. The findings establish a new paradigm in biosensing, demonstrating that the synergy between multi-resonance data acquisition and ML-based analysis can significantly enhance detection accuracy. This study provides a scalable pathway for advancing high-precision biosensing technologies.