AFM Image Deconvolution Software User s Manual

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1 M. C. Goh Research Group Version 2.0 AFM Image Deconvolution Software User s Manual

2 Table of Content Introduction Contact M. C. Goh Research Group... 3 Zod Information... 3 Installing Zod Downloading files... 4 MCRInstaller.exe... 4 AFM.ctf... 4 AFM.exe... 4 Using Zod Graphic User Interface... 5 Importing AFM Image... 6 Pyramid AFM Probe Construction... 6 AFM Image Deconvolution... 7 Probe Reconstruction

.. 4 AFM.ctf... 4 AFM.exe... 4 Using Zod Graphic User Interface.

3 Introduction Thank you for choosing Zod version 2.0 AFM image deconvolution software. Zod provides a way of visualizing the AFM sample and probe tip as well as reducing distortions in AFM image. This manual has been provided to get you started. Contact M. C. Goh Research Group Contact the software developer, Tian Mu Liu if you have any questions or if you should encounter any difficulty while using Zod. Contact by at tliu@chem.utoronto.ca Zod Information One of Zod s goals is to provide users with the ability to reduce distortions of Atomic Force Microscopy images due to finite size of AFM probe. We developed this software tool by Matlab due to its superior functionalities of matrix manipulations. The software is developed to support ASCII files that are exported from Nanoscope 5.30 or Nova. Also, the resultant files can be imported by Nanoscope 5.30 and Nova. Other ASCII file formats could be implemented easily in future if necessary. We encourage users to report any errors found in our program by the software developer. 3

Contact by e-mail at tliu@chem.utoronto.

4 Installing Zod Zod contains three pieces of programs to run properly. Downloading Files 1. Go to 2. The user is required to download MCRInstaller.exe if Matlab is not installed on the PC 3. Download and unzip Zod.rar MCRInstaller.exe This installer contains Matlab libraries. Installing this program allows Windows to run Matlab script-based stand alone files. If you already have Matlab installed, there is no need to install this program. If not, simply download and install it once. AFM.ctf This file contains Matlab scripts for AFM image deconvolution. It must be placed in the same folder as AFM.exe. AFM.exe To initiate Zod, double click AFM.exe. Make sure AFM.ctf is in the same folder. Zod user interface will come up in few seconds. 4

Installing this program allows Windows to run Matlab script-based stand alone files. If you already have Matlab installed, there is no need to install this program.

5 Using Zod Graphic User Interface GUI is constructed for users to use this program without any programming knowledge. Part A: Choose the format of the input file as Nanoscope 5.30 or Nova. Part B: Load the AFM sample file into a matrix. Ask user the address of input file, max height of sample if the input file is Nanoscope 5.30 format. Part C: Display matrix in 3 dimension graphic platform. Users can zoom in and out, and rotate the image. This command button triggers plotafm.m. Part D: Choose the source of AFM probe construction. Quadrilateral pyramid, probe convolution, load saved probe options are available. Part E: Triggers the options chosen in Part D. quadrilateral pyramid option collects three parameters that defines an ideal probe. Probe convolution option brings up probe convolution interface, which will be explained later. Load saved probe option asks user the address of the matrix of AFM probe saved by the user, and load it into a matrix. Part F: Rotate the AFM probe 90 degree clockwise. It is an option for user to correspond to an asymmetric AFM probe. Part G: Save the AFM probe as an ASCII text file on a location on hard drive. Part H: Triggers deconvolution algorithm on AFM sample image. Part I: Save the resultant AFM sample image as the same format as input text file. This allows the user to use the resultant file with other software. 5

Part C: Display matrix in 3 dimension graphic platform. Users can zoom in and out, and rotate the image. This command button triggers plotafm.m. Part D: Choose the source of AFM probe construction.

6 Importing AFM Image The input file we worked with is ASCII key text file exported by Nanoscope 5.30 or Nova. To construct 3D representation of the sample, the following data is inputted from text file. Information Implemented Useful Information External Information Required Nanoscope 5.30 text file Description and matrix of sample height, description and matrix of phase shift Matrix of sample height, dimension of square sample size, the difference between highest point and lowest point User needs to input the actual height in nm for the difference between highest point and lowest point Nova text file Description and matrix of sample height Matrix of sample height, scale of x, y and z dimension unit, number of data points in x, y dimensions N/A Script Applied importafm.m importafm2.m Pyramid AFM Probe Construction Construct a square based pyramid AFM probe with a spherical apex in 3D digital form. This is the shape an ideal AFM probe should be. The probe will be constructed base on three parameter entered by the user. These parameters are a (side of square base), h (height of probe tip), and r (radius of spherical apex). 6

30 text file Description and matrix of sample height, description and matrix of phase shift Matrix of sample height, dimension of square sample size, the difference between highest point and lowest

7 AFM Image Deconvolution Background knowledge As AFM probe moves across the surface of a sample, it is prematurely lifted if the side of the probe is in contact with the sample instead of the apex. This lift introduces an error region in addition to the region of sample. See image below for geometric representation (the parabola represents AFM probe, circle represents sample). Note: Larger or more irregular the tip, greater the error. Error region is a product of the geometry of the tip. Deconvolution Process A portion of error can be determined given the knowledge of the shape of the probe. See image below for geometric representation (the parabola represents AFM probe, circle represents sample). Note: Placing the apex of AFM probe on a point on AFM image, the overlapping between them is absolute error. Eliminating this region and repeat this process at all points on the AFM image will give a sharper and more accurate AFM image. This deconvolution program works with three-dimensional images with the same concept. For more detailed description, one can read AFM Probe Tip Visualization and Improvement of Images by a Simple Deconvolution Procedure by P. Markiewicz, M. C. Goh. 7

Note: Larger or more irregular the tip, greater the error. Error region is a product of the geometry of the tip.

8 Probe Reconstruction Since the shape of AFM probe determines the distortion and error of a given AFM image, an accurate construction of AFM probe must be achieved. Although the assumption of an AFM probe as square pyramid of various radiuses for the spherical apex is decent, it does not take into the account of manufacture defects, damaged or messy probes. Thus, reconstructing the shape of the actual probe is required. Since an AFM image of a sphere is the combination of the sphere and the error produced by the probe, the image of probe can be constructed by deconvoluting the AFM image with the actual image of the sphere. An image of the probe can be constructed by AFM a sample of known dimensions. In this case, the sample is a polymer spherical bead with diameter of 500 nm. The scripts importafm_b.m and importafm2_b.m are used to load AFM image of a spherical bead. The scripts makeprobe.m and makeprobe2.m are used to create an ideal spherical bead determined by its radius (inputted by user). The script devafm.m is used for deconvolution procedure to create the image of AFM probe. Probe Convolution Interface contains all the functions to reconstruction AFM probe. It is similar to AFM Deconvolution Interface. 8

Thus, reconstructing the shape of the actual probe is required.

Application of Non-Linear Equalization for Characterizing AFM Tip Shape

Application of Non-Linear Equalization for Characterizing AFM Tip Shape Dipl.-Ing. T. Machleidt, PD Dr.-Ing. habil. K.-H. Franke, D. Kapusi, T. Langner Computer Graphics Group / TU-Ilmenau Nanopositionier-