Detection of Tip-Sample Interaction in Atomic Force Microscopy

Detection of Tip-Sample Interaction in Atomic Force Microscopy: Improving the Image Resolution

Noom, Jacques (TU Delft Mechanical, Maritime and Materials Engineering)

Verhaegen, Michel (mentor)
Smith, Carlas (graduation committee)
Giordano, Giulia (graduation committee)
Alijani, Farbod (graduation committee)
Katan, Allard (graduation committee)

Delft University of Technology

Mechanical Engineering | Systems and Control

2019-07-05

Currently used imaging methods in Atomic Force Microscopy (AFM) including the use of a Lock-In Amplifier or a Phase-Locked Loop, are suboptimal. In this report, the image resolution in AFM is improved by detecting the tip-sample interaction using complete measurements of the input of the cantilever and its measured deflection. Two methods are studied while assuming that the tip-sample interaction is sparse, namely a model-based approach and a data-driven approach. Real-life experiments have shown that the model-based approach improves the image resolution with a factor of 7.5 to 0.555 nm compared to the conventional imaging method, according to a metric using Fourier Ring Correlation in which a reference image is unnecessary. The data-driven approach can be used in the model-based approach to further improve the resolution. In addition to improved resolutions, a Linear Time-Invariant model of the mechanically driven AFM-cantilever immersed in liquid – from piezo input to cantilever deflection – has been obtained through subspace identification with a Variance Accounted For of 79.2%. Recommendations for future research include applying the latter model in detecting the tip-sample interaction, improving the data-driven approach, reducing the computational effort of the model-based approach and implementing algorithms for detecting the tip-sample interaction online.

Atomic Force Microscopy
System Identification
LASSO
Fourier Ring Correlation
Difference of Convex Programming
Bilinear
State Estimation

http://resolver.tudelft.nl/uuid:8f318a42-a9ff-4c88-b2c4-7c69e25d3209

Student theses

master thesis

© 2019 Jacques Noom