Optimized Signal Estimation in Nanomechanical Photothermal Sensing via Thermal Response Modelling and Kalman Filtering

TL;DR

This paper introduces a thermal response model combined with adaptive Kalman filtering for nanomechanical photothermal sensing, significantly improving speed and accuracy in IR spectroscopy applications.

Contribution

It develops a novel thermal response model and an optimized Kalman filter tailored for FPGA systems, enhancing real-time photothermal signal estimation in nanomechanical sensors.

Findings

Enhanced response speed over standard filters

Reduced drift and noise effects in measurements

Improved IR spectrum accuracy and speed

Abstract

We present an advanced thermal response model for micro- and nanomechanical systems in photothermal sensing, designed to balance speed and precision. Our model considers the two time constants of the nanomechanical element and the supporting chip, triggered by photothermal heating, enabling precise photothermal input signal estimation through Kalman filtering. By integrating heat transfer and noise models, we apply an adaptive Kalman filter optimized for FPGA systems in real-time or offline. This method, used for photothermal infrared (IR) spectroscopy with nanomechanical resonators and a quantum cascade laser (QCL) in step-scan mode, enhances response speed beyond standard low-pass filters, reducing the effects of drift and random walk. Analytical calculations show the significant impact of the thermal expansion coefficients' ratio on the frequency response. The adaptive Kalman filter, informed by the QCL's input characteristics, accelerates the system's response, allowing rapid and precise IR spectrum generation. The use of the Levenberg-Marquardt algorithm and PSD analysis for system identification further refines our approach, promising fast and accurate nanomechanical photothermal sensing.