Real-Time Estimation of the Tip-Sample Interactions in Tapping Mode Atomic Force Microscopy with a Regularized Kalman Filter

Real-Time Estimation of the Tip-Sample Interactions in Tapping Mode Atomic Force Microscopy with a Regularized Kalman Filter

Keyvani Janbahan, A.
van der Veen, G.
Tamer, M.S.
Sadeghian Marnani, H.
Goosen, H.
van Keulen, F.

2020

The real-time and accurate measurement of tip-sample interaction forces in Tapping Mode Atomic Force Microscopy (TM-AFM) is a remaining challenge. This obstruction fundamentally stems from the causality of the physical systems. Since the input of the dynamic systems propagates to the output with some delay, and multiple different inputs can generate the same output, there exist no measurement or estimation technique that can estimate the force input of the systems in real-time without phase and amplitude distortion. However, an approximate and delayed estimation can still be possible. This article presents a general-purpose algorithm which aims to estimate an approximation of the force input of TM-AFM with minimum delay and error. For this reason, first, the input estimation problem is converted to an ill-posed state observation problem. Then, a Tikhonov-like regularization technique is applied to eliminate the ill-conditioning and estimate the force input using a linear Kalman filter. The proposed input observer is remarkably robust, real-time in the order of the sampling frequency, and applicable for any Linear Time Invariant (LTI) system with a (semi-)periodic process. Simulation and experimental results show that using the proposed algorithm with a wide-band AFM probe; one can determine the tip-sample forces with only a few percent error and a delay in the order of sampling time. Unlike the existing force estimation techniques for AFM, this algorithm does not require any prior knowledge of the force-distance relationship which can be very beneficial for the closed-loop control of AFM.

High Tech Systems & Materials
Industrial Innovation
Multi-harmonic AFM
Unknown input estimation
Approximation algorithms
Kalman filters
Amplitude distortions
Estimation techniques
Linear Kalman filters
Linear time-invariant system
Regularization technique
Tapping mode atomic force microscopies (TM-AFM)
Tip-sample interaction
Real time systems

http://resolver.tudelft.nl/uuid:ef0a6ae2-a39a-49f1-b3d9-845ed0d1393a

https://doi.org/10.1109/tnano.2020.2974177

875757

Institute of Electrical and Electronics Engineers IEEE

1536-125X

IEEE Transactions on Nanotechnology, 19, 274-283

9011753

article