Intelligent optical microresonator imaging sensor for early stage classification of dynamical variations

Abstract

Although machine learning (ML) solutions are conquering science and technology with a growing number of the affected branches, only a few examples have been demonstrated so far to boost the highly sensitive and label-free optical detectors. Nevertheless, ML methods can indeed provide novel strategies for emerging optical sensing technologies to overcome the challenges of real-world implementation. On this way, the sensing techniques become intelligent self-learning systems capable for predictions of the probed parameters in an automated and self-adjusting manner. Herein, a report on the upgraded ML engine of an affordable multiresonator imaging sensor operating at a fixed frequency is provided. It is demonstrated for the first time that such a sensor supplemented by a long short-term memory (LSTM) network processing engine enables accurate prediction of the dynamical sensor responses already within the early stages of the observed variations. On example of the experimental data of solutions mixing dynamics, the possibility for accurate (>95%) differentiation of the dynamical variations at the refractive index level of 5.5 × 10−4 within a six times shorter period than the whole time series is shown. The impact of the parameters and architecture of the LSTM-based processing engine on the speed and prediction accuracy is analyzed.