Reinforcement Learning Based Sensor Optimization for Bio-markers

TL;DR

This paper introduces a reinforcement learning-based optimization method to improve the sensitivity of RFID biosensors, outperforming existing algorithms in design parameter optimization across multiple frequency ranges.

Contribution

It presents a novel reinforcement learning approach, RLBPSO, for optimizing IDC-based RF sensor design parameters, demonstrating superior performance over traditional methods.

Findings

RLBPSO outperforms ACO and other methods in sensor sensitivity enhancement.

Optimized electrode design and finger width improve sensor performance.

The method is effective across various frequency ranges.

Abstract

Radio frequency (RF) biosensors, in particular those based on inter-digitated capacitors (IDCs), are pivotal in areas like biomedical diagnosis, remote sensing, and wireless communication. Despite their advantages of low cost and easy fabrication, their sensitivity can be hindered by design imperfections, environmental factors, and circuit noise. This paper investigates enhancing the sensitivity of IDC-based RF sensors using novel reinforcement learning based Binary Particle Swarm Optimization (RLBPSO), and it is compared to Ant Colony Optimization (ACO), and other state-of-the-art methods. By focusing on optimizing design parameters like electrode design and finger width, the proposed study found notable improvements in sensor sensitivity. The proposed RLBPSO method shows best optimized design for various frequency ranges when compared to current state-of-the-art methods.