|About this Abstract
||Materials Science & Technology 2019
||Data Science for Material Property Interpretation
||Neural Networks for Processing of Low Signal-to-noise Data in Scanning Probe Microscopy
||Nikolay Borodinov, Sabine Neumayer, Sergei V Kalinin, Olga S Ovchinnikova, Rama K Vasudevan, Stephen Jesse
|On-Site Speaker (Planned)
Functional fitting of the materials response is a common approach to extract physical properties. With the recent developments in scanning probe microscopy instrumentation, such response can be acquired at nanoscale level providing ability to map relevant parameters across the image and develop a deeper understanding of the systems under study. As signal-to-noise ratio decreases, the functional fits done using traditional iterative methods become very sensitive to initial guesses and yield spurious results. Here, we demonstrate a neural network-based approach for signal processing which allows for effective extraction of physical parameters at small driving signals or when a material’s response is weak.
||Definite: At-meeting proceedings