Multi-Output Convolutional Neural Network for Improved Parameter Extraction in Time-Resolved Electrostatic Force Microscopy Data

TL;DR

This paper introduces a multi-output CNN that improves parameter extraction from time-resolved electrostatic force microscopy data, accurately modeling complex dynamics beyond single-exponential assumptions and handling noisy experimental signals.

Contribution

It presents a novel multi-branched, multi-output CNN that incorporates physical parameters, enhancing the analysis of complex, real-world trEFM signals over previous empirical and simulated-data methods.

Findings

Accurately extracts parameters for single- and bi-exponential functions.

Reconstructs experimental data more accurately in noisy conditions.

Demonstrates physics-informed machine learning for complex signal processing.

Abstract

Time-resolved scanning probe microscopy methods, like time-resolved electrostatic force microscopy (trEFM), enable imaging of dynamic processes ranging from ion motion in batteries to electronic dynamics in microstructured thin film semiconductors for solar cells. Reconstructing the underlying physical dynamics from these techniques can be challenging due to the interplay of cantilever physics with the actual transient kinetics of interest in the resulting signal. Previously, quantitative trEFM used empirical calibration of the cantilever or feed-forward neural networks trained on simulated data to extract the physical dynamics of interest. Both these approaches are limited by interpreting the underlying signal as a single exponential function, which serves as an approximation but does not adequately reflect many realistic systems. Here, we present a multi-branched, multi-output convolutional neural network (CNN) that uses the trEFM signal in addition to the physical cantilever parameters as input. The trained CNN accurately extracts parameters describing both single-exponential and bi-exponential underlying functions, and more accurately reconstructs real experimental data in the presence of noise. This work demonstrates an application of physics-informed machine learning to complex signal processing tasks, enabling more efficient and accurate analysis of trEFM.