Dynamic Characterization of a Fast-responding Nanophotonic Gas Sensor using Optimization-based System Identification

This paper develops and parameterizes a dynamic model of a novel nanophotonic gas sensor. The sensor employs a functionalized microring resonator to measure CO 2 concentrations in biomedical applications. The literature presents both computational and experimental approaches for characterizing nanophotonic sensor response times. However, it can be challenging to distinguish between the dynamics of a fast-responding sensor versus the dynamics of the setup used for characterizing it. We address this challenge using optimization-based system identification. Specifically, we construct a test rig that supplies mixed N 2 /CO 2 flow to the sensor and measures the sensor's voltage amplitude response at a given laser excitation wavelength. Step response experiments are conducted using this test rig, at different gas flowrates and concentrations. A state-space model is then constructed, capturing the sensor's first-order dynamics as well as the gas transport, manifold filling, and first-order valve actuator dynamics of the test setup. A particle swarm optimizer is used for least-squares model parameterization. The resulting residuals are reasonable in magnitude, two observations being that their magnitude changes with CO 2 concentration and that they exhibit some coloring, potentially due to the setup's signal processing filters. The estimated time constant has reasonable Cramér-Rao bounds, and is close in magnitude to finite element predictions.