Plasmonic sensor using generative adversarial networks integration

Abstract

Machine learning (ML) has emerged as a pivotal force in enhancing the capabilities of sensing technologies across a broad spectrum of applications, from environmental monitoring and biosensing to agriculture, industrial automation, and so on. This study explores integrating ML techniques with photonic crystal fiber (PCF)-based plasmonic sensing techniques to elevate sensor performance. The PCF has two open channels to augment mode coupling, effectively reducing the gap between the analyte channel and core. Moreover, a thin layer of gold within the open channels of the PCF initiates efficient plasmon generation. The results demonstrate a maximum wavelength sensitivity of 9000 nm/refractive index unit (RIU), which can detect a wide range of analyte refractive index (RI) values from 1.33 to 1.40. The sensor exhibits the maximum amplitude sensitivity of 490.41 RIU−1. It also boasts a resolution of 1.11 × 10−5 RIU and the maximum figure-of-merit (FOM) achieved is 138.04 RIU−1 at an analyte RI of 1.39. Furthermore, this research introduces a method utilizing generative adversarial networks (GAN) to expand training data for an artificial neural network (ANN) model. This approach substantially improves the prediction of confinement loss across various analytes and wavelengths in a unique geometric configuration. The sensor’s versatility makes it ideal for various applications, including chemical sensing and medical diagnostics.