Crackling noise microscopy

The authors present crackling noise microscopy, a method for measurement of the crackling of individual nanoscale features based on AFM nanoindentation. They use it to investigate crackling noise and avalanches in the domains and domain walls of ferroelectric materials. Crackling noise is a scale-invariant phenomenon found in various driven nonlinear dynamical material systems as a response to external stimuli such as force or external fields. Jerky material movements in the form of avalanches can span many orders of magnitude in size and follow universal scaling rules described by power laws. The concept was originally studied as Barkhausen noise in magnetic materials and now is used in diverse fields from earthquake research and building materials monitoring to fundamental research involving phase transitions and neural networks. Here, we demonstrate a method for nanoscale crackling noise measurements based on AFM nanoindentation, where the AFM probe can be used to study the crackling of individual nanoscale features, a technique we call crackling noise microscopy. The method is successfully applied to investigate the crackling of individual topological defects, i.e. ferroelectric domain walls. We show that critical exponents for avalanches are altered at these nanoscale features, leading to a suppression of mixed-criticality, which is otherwise present in domains. The presented concept opens the possibility of investigating the crackling of individual nanoscale features in a wide range of material systems.

Automated summary

The authors propose a method called crackling noise microscopy, which is based on AFM nanoindentation, to measure the crackling of individual nanoscale features. They use this method to study crackling noise and avalanches in ferroelectric materials. The crackling noise is a scale-invariant phenomenon found in various nonlinear dynamical material systems, and avalanches follow universal scaling rules described by power laws. The concept of crackling noise is widely used in different fields such as earthquake research and neural networks.