The University of Southampton
University of Southampton Institutional Repository

Control of atomic force microscopes

Control of atomic force microscopes
Control of atomic force microscopes
Atomic force microscopes or AFMs are instruments which use a mechanical probe to scan a sample and estimate surface topography with nanometer accuracy. The term atomic force originates from the fact that the imaging process relies upon the existence of the inter-atomic interaction force between the mechanical probe and sample surface. These instruments have established themselves as a vital cutting edge tool for investigation of matter at the nanometer scale. Their widespread usage is due not only to their superior resolution but also because they can operate in any medium namely air, liquid and vacuum. Another major advantage is that, unlike their predecessor instruments AFMs do not require their samples to be conductive. This fact alone has enabled in situ imaging of biological samples with unprecedented resolution and without sample alteration. Other instruments like scanning electron microscopes (SEMs) can also view biological samples, however they require the samples to be prepared and dried. While some sample structure may be preserved, AFMs have no such limitation. Despite the fact that AFMs offer all these advantages, the usage of a mechanical probe for image generation causes them to be inherently reliant upon a feedback control loop. This is because the probe motion must be controlled in a suitable manner to avoid letting its motion dynamics distort the sample image. In addition, since the mechanical probe must be sequentially moved over the sample point by point, the imaging times are long and range from a few seconds to in excess of ten minutes. Given that feedback control is an integral part of AFM operation, the end users are forced to manually tune Proportaional-Integral (PI) controllers which are used in most commercial AFMs. Since the vast majority of scientists using AFMs do not necessarily possess a knowledge of feedback control, they do this tuning though a manual trial and error procedure which consumes valuable research time. Although the control systems community has taken considerable interest in AFM control, the methods suggested often require high order controllers and are tested for a specific experimental set up. The primary objective of this research is therefore to develop a novel automated controller synthesis mechanism which has the potential of being used in a diverse range of AFM setups. The method of choice for this research is Multiple Model Adaptive Control (MMAC). The motivation for this decision as well as experimental verification is provided in detail in this thesis. Given the wide commercial usage of PI controllers, the same are used as a starting point for this work. The applicability of the method suggested is however by no means restricted to them, and in the future can be extended to incorporate more sophisticated controllers, for instance robust controllers. The second objective of this research is to investigate two novel methods which have the potential of substantially reducing the AFM imaging time. The first one suggests coarser scan trajectories to save time, and then estimates the sample image using a relatively new signal processing method called Compressive Sensing. The second method suggested uses the AFM's mechanical probe in a novel manner that can also substantially reduce imaging time.
Khan, Umar
1ed9e27f-c236-4889-80b8-d7cdc4dfc0f5
Khan, Umar
1ed9e27f-c236-4889-80b8-d7cdc4dfc0f5
French, Mark
22958f0e-d779-4999-adf6-2711e2d910f8

Khan, Umar (2014) Control of atomic force microscopes. University of Southampton, Faculty of Physical Sciences and Engineering, Doctoral Thesis, 171pp.

Record type: Thesis (Doctoral)

Abstract

Atomic force microscopes or AFMs are instruments which use a mechanical probe to scan a sample and estimate surface topography with nanometer accuracy. The term atomic force originates from the fact that the imaging process relies upon the existence of the inter-atomic interaction force between the mechanical probe and sample surface. These instruments have established themselves as a vital cutting edge tool for investigation of matter at the nanometer scale. Their widespread usage is due not only to their superior resolution but also because they can operate in any medium namely air, liquid and vacuum. Another major advantage is that, unlike their predecessor instruments AFMs do not require their samples to be conductive. This fact alone has enabled in situ imaging of biological samples with unprecedented resolution and without sample alteration. Other instruments like scanning electron microscopes (SEMs) can also view biological samples, however they require the samples to be prepared and dried. While some sample structure may be preserved, AFMs have no such limitation. Despite the fact that AFMs offer all these advantages, the usage of a mechanical probe for image generation causes them to be inherently reliant upon a feedback control loop. This is because the probe motion must be controlled in a suitable manner to avoid letting its motion dynamics distort the sample image. In addition, since the mechanical probe must be sequentially moved over the sample point by point, the imaging times are long and range from a few seconds to in excess of ten minutes. Given that feedback control is an integral part of AFM operation, the end users are forced to manually tune Proportaional-Integral (PI) controllers which are used in most commercial AFMs. Since the vast majority of scientists using AFMs do not necessarily possess a knowledge of feedback control, they do this tuning though a manual trial and error procedure which consumes valuable research time. Although the control systems community has taken considerable interest in AFM control, the methods suggested often require high order controllers and are tested for a specific experimental set up. The primary objective of this research is therefore to develop a novel automated controller synthesis mechanism which has the potential of being used in a diverse range of AFM setups. The method of choice for this research is Multiple Model Adaptive Control (MMAC). The motivation for this decision as well as experimental verification is provided in detail in this thesis. Given the wide commercial usage of PI controllers, the same are used as a starting point for this work. The applicability of the method suggested is however by no means restricted to them, and in the future can be extended to incorporate more sophisticated controllers, for instance robust controllers. The second objective of this research is to investigate two novel methods which have the potential of substantially reducing the AFM imaging time. The first one suggests coarser scan trajectories to save time, and then estimates the sample image using a relatively new signal processing method called Compressive Sensing. The second method suggested uses the AFM's mechanical probe in a novel manner that can also substantially reduce imaging time.

Text
Khan.pdf - Other
Download (8MB)

More information

Published date: November 2014
Organisations: University of Southampton

Identifiers

Local EPrints ID: 372495
URI: http://eprints.soton.ac.uk/id/eprint/372495
PURE UUID: 48d5a6c3-989c-417f-8246-7defdcbf85c3

Catalogue record

Date deposited: 23 Dec 2014 14:46
Last modified: 18 Nov 2019 20:28

Export record

Contributors

Author: Umar Khan
Thesis advisor: Mark French

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×