Introduction to Image Processing and Analysis. Applications Scientist Nanotechnology Measurements Division Materials Science Solutions Unit

Transcription

1 Introduction to Image Processing and Analysis Gilbert Min Ph D Gilbert Min, Ph.D. Applications Scientist Nanotechnology Measurements Division Materials Science Solutions Unit

2 Working with SPM Image Files Raw data files (binary / ASCII formats) Limited tools for display & analysis Realtime acquisition Post processing software Roundness..... ISO Height Parameters Sq. Ssk -. Sku. Sp. Element: segment of width: mm, Enclosed area:. mm Agilent PicoImage - mm Results for presentation/publication (.jpg,.tiff,.avi,.xls, etc.)

3 First Step: Image Leveling Most all SPM images require a basic leveling to remove inevitable artifacts from image acquisition (sample tilt, scanner bow / nonlinearities, z-drift, line skips, etc.). Original raw image After leveling process Common approaches to leveling: - plane flattening - line by line flattening

4 Leveling Images: Plane Flatten Simplest approach a linear plane is subtracted from surface LS plane fit..... Useful when there is very minimal curvature relative to the surface topography

5 Plane Flattening: -Point Method Plane is simply defined d by three user-defined d reference points on the surface Useful for step height applications, where a user specific leveling reference is required and where the surface can be leveled to an average.

6 Line Flattening Each scan line is fit to a polynomial and the polynomial shape is subtracted. st order Z X The height average of each line is set equal to the previous line to remove any offset nd order Z X Z rd order X scan lines leveled line

7 Line Flattening: a Cylindrical Hair Follicle th order (raw) st order nd order

8 Using Include/Exclude with Line Flattening st order st order excluding raised stamps Line by line levelled Artifacts from line flattening can be avoided by identifying structures to include/exclude in the calculated polynomial used in subtraction

9 D / D Display Options Color Pallette Add Visualization Effects D continuous mesh D copper material D photo simulation

10 Adding Data Overlay onto D Surfaces More info can be extracted when combining multiple data channels - surface topography with functional imaging (phase, KFM, EFM, MFM, etc.) V topography. surface potential. = D overlay SDRAM SP overlaid on topography PZT film SP overlaid on topography Organic material phase overlaid on topography

11 Filtering: Removing Noise from Images Using a filtering algorithm can remove unwanted noise that often appears in acquired images Matrix / Spatial Filtering Spatial filtering is made by moving a transformation matrix over the surface. Input I pixels are interpolated/modified according to the weighted values of adjacent pixels to produce filtered image of output O pixels Types of Matrix Filters: -Smoothing/denoising (median, mean, Gaussian) -Min/Max -Edge detection (Laplacian, Sobel, Gradient) -Many more including custom user-defined! x Gaussian Filter A Custom x? No effect: every pixel is multiplied by

12 Applying Matrix / Spatial Filters.. median denoising x Sobel x edge detection median denoising x

13 Filtering: Removing Noise from Images Fourier filtering Calculates a spectral representation of frequency components (FFT) of an image and user identifies bandwidths for inclusion/exclusion into the filtered surface. Useful for images with periodic patterns, eg. atomic lattices Raw data ( o line level) D FFT spectrum FFT filtered

14 Analysis Tools: Profile Extraction / Step Height Extracted profile Maximum height Mean height Width..... Total height v-p-v Total height v-p Minimum height Extracted profile Maximum height Mean height Width..... Total height v-p-v Total height v-p Minimum height

15 Measuring Surface Roughness Roughness parameters quantify height statistics of a surface Some commonly reported values Root Mean Square Standard deviation of the height distribution Arithmetic Mean Skewness Kurtosis Maximum peak height Mean surface height st moment of distribution rd statistical moment, qualifying the symmetry of distribution th statistical moment describing flatness of distribution Height between the highest peak and the mean plane EUR and ISO Standards exist for D & D parameters to ensure conformity Maximum pit height Maximum height Depth between the mean plane and the deepest valley Heightbetween the highest peak and the deepest valley

16 Surface Roughness Examples ISO Height Parameters Sq. Ssk. Sku. Sp. Sv. Sz. Sa. ISO Smooth film Pitted film Height Parameters Sq. Ssk -. Sku. Sp. Sv. Sz. Sa.

17 Surface Roughness: Same Surface, Different Scan Sizes um scan ISO Height Parameters Sq. Ssk. Sku. Sp. Sv. Sz. Sa. um scan ISO Height Parameters Sq. Ssk. Sku. Sp. Sv. Sz Sa. Important calculations are made over appropriate p length scales, as roughness values depend on sample size

18 Using the Thresholding Tool Allows user to select surface planes of different altitudes/height levels for manipulation % % Place along curve corresponds to height level % Abbott Firestone Curve (height histogram & bearing ratio)

19 Using the Thresholding Tool Stamp substrate t ISO Height Parameters Sa. Sq. Sp. Sv. Sz. Top surface of stamp bits ISO Height Parameters Sa. Sq. Sp. Sv. Sz Stamp bits including sidewall ISO Height Parameters Sa. Sq. Sp. Sv. Sz.

20 Example Workflow for Pore Analysis % %. Thresholded -.. Choose proper flattening method. Use height thresholding tool to select pits of interest Form factor. Mean parameters on grains... Number of grains: Total area occupied by the grains:. (. %) Density of grains:. grains /. Area =. +/-. Perimeter = +/- Mean diameter Mean diameter =. +/-. Min diameter =. +/-. Max diameter =. +/- Form factor =. +/-. Aspect ratio =. +/-.. Roundness =. +/-. Orientation =. +/-... Binarization defines pores for. Display results..

21 Always remember When working with images, it s good practice to: ) Preserve raw data files before applying operators & filters ) Keep a consistent workflow among data sets, especially when comparing statistical results ) Try to avoid over-processing data and introducing ) Try to avoid over processing data and introducing artificial software image artifacts