Machine Learning in Impedance-Based Sensors

Abstract

The impedance technique is deployed to understand the electrical prop-erties of various conducting surfaces and their interfaces. It is an analytical tool that is applied in electrochemistry and is commonly referred to as Electrochem-ical Impedance Spectroscopy (EIS). Impedance is applied in many fields such as sensors, semiconductors, energy storage devices, corrosion technology, conducting polymers, coatings, ceramics, and advanced materials. Material properties and func-tions change with a given environment along with the stability of the system. EIS is a complex system and the output curves are represented as Nyquist and Bode plots. The data obtained from the plots have to be treated with mathematical and electrical components to arrive at the equivalent circuit to meaningfully interpret it. Equiva-lent circuits analysis helps us to interpret the mechanism of the device in the given electrochemical system. Machine learning (ML) tools help us to train the systems to process the data and obtain the perfect matching equivalent circuit but several challenges remain as EIS database creation is the biggest challenge.