Image Processing with Scilab and Image Processing Design Toolbox
Image Processing 2 - Agilent · Introduction to Image Processing and Analysis Gilbert Min, Ph.D....
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Introduction to Image Processing and Analysis Gilbert Min, Ph.D. Applications Scientist Nanotechnology Measurements Operations
Transcript of Image Processing 2 - Agilent · Introduction to Image Processing and Analysis Gilbert Min, Ph.D....
Introduction to Image Processing and
Analysis
Gilbert Min, Ph.D.Applications ScientistNanotechnology Measurements Operations
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Isotropy: 2.22 %
First Direction: 67.5°
Second Direction: 114°
Third Direction: 90.0°
Working with SPM Image Files
Realtime acquisition
Post processing software
Raw data files (binary / ASCII formats)Limited tools for display & analysis
Agilent PicoImageResults for presentation/publication(.jpg, .tiff, .avi, .xls, etc.)
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Elem ent: segm ent o f wid th : 1 m m , E nclosed area : 0 .0653 m m 2
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Roundness
ISO 25178Height Parameters
Sq 3.19
Ssk -0.945
Sku 3.85
Sp 4.77
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.).
Common approaches to leveling:- plane flattening- line by line flattening
Original raw image After leveling process
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Leveling Images: Plane FlattenSimplest approach – a linear plane is subtracted from surface
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Useful when there is very minimal curvature relative to the surface topography
LS plane fit
Plane Flattening: 3-Point Method
Plane is simply defined by three user-defined 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.
Line Flattening
Each scan line is fit to a polynomial and the polynomial shape is subtracted.
The height average of each line is set equal to the previous line to remove any offset
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scan lines
leveled line
Line Flattening: a Cylindrical Hair Follicle0 2 4 6 8 10 µm
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Using Include/Exclude with Line Flattening
Artifacts from line flattening can be avoided by identifying structures to include/exclude in the calculated polynomial used in subtraction
Line by line levelled
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1st order 1st order excluding raised stamps
2D / 3D Display OptionsColor Pallette
3D continuous mesh 3D copper material
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Add Visualization Effects
2D photo simulation
Adding Data Overlay onto 3D SurfacesMore info can be extracted when combining multiple data channels - surface topography with functional imaging (phase, KFM, EFM, MFM, etc.)
Organic material phase overlaid on topography
PZT filmSP overlaid on topography
SDRAMSP overlaid on topography
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surface potentialtopography
+ =
3D overlay
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 Ipixels 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!
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A “Custom” 3x3?
No effect:every pixel is
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3x3 GaussianFilter
Applying Matrix / Spatial Filters0 1 2 3 4 5 µm
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median denoising7x7
median denoising27x27
Sobel 7x7edge detection
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
Filtering: Removing Noise from Images
Raw data (1o line level)
2D FFT spectrum FFT filtered
Analysis Tools: Profile Extraction / Step Height
Extracted profile
Extracted profile
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Maximum height 150 nm 152 nm 162 nm 161 nm 112 nm
Mean height 141 nm 149 nm 158 nm 154 nm 111 nm
Width 0.414 µm 0.429 µm 0.443 µm 0.414 µm 0.343 µm
Total height v-p-v 165 nm 167 nm 170 nm 181 nm 135 nm
Total height v-p 142 nm 167 nm 170 nm 154 nm 133 nm
Minimum height 133 nm 145 nm 155 nm 145 nm 109 nm
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Maximum height 168 nm 153 nm 153 nm 150 nm 150 nm
Mean height 157 nm 152 nm 151 nm 148 nm 148 nm
Width 0.415 µm 0.415 µm 0.415 µm 0.401 µm 0.386 µm
Total height v-p-v 172 nm 157 nm 155 nm 154 nm 154 nm
Total height v-p 170 nm 157 nm 155 nm 153 nm 154 nm
Minimum height 149 nm 150 nm 150 nm 145 nm 147 nm
Measuring Surface Roughness Roughness parameters quantify height statistics of a surface
Some commonly reported values
Depth between the mean plane and the deepest valley
Maximum pit height
Height between the highest peak and the mean plane
Maximum peak height
Heightbetween the highest peak and the deepest valley
Maximum height
4th statistical moment describing flatness of
distribution
Kurtosis
3rd statistical moment, qualifying the symmetry of
distribution
Skewness
Mean surface height
1st moment of distribution
Arithmetic Mean
Standard deviation of the height distribution
Root Mean Square
EUR and ISO Standards exist for 2D & 3D parameters to ensure conformity
Surface Roughness Examples
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ISO 25178Height Parameters
Sq 1.78 nm
Ssk -3.04
Sku 18.4
Sp 5.97 nm
Sv 15.3 nm
Sz 21.3 nm
Sa 1.06 nm
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Sq 0.268 nm
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“Smooth” film “Pitted” film
Surface Roughness: Same Surface, Different Scan Siz esnm
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Sq 10.5 nm
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Sp 35.7 nm
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Sz 61.1 nm
Sa 8.91 nm
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Sq 8.38 nm
Ssk 0.74
Sku 5.89
Sp 96.8 nm
Sv 27.5 nm
Sz 124 nm
Sa 6.22 nm
Important calculations are made over appropriate length scales, as roughness values depend on sample size
5 um scan
25 um scan
Using the Thresholding Tool
Allows user to select surface planes of different altitudes/height levels for manipulation
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Abbott – Firestone Curve(height histogram & bearing ratio)
Place along curve corresponds to height level
Using the Thresholding Tool
Stamp substrate ISO 25178Height Parameters
Sa 2.94 nm
Sq 3.9 nm
Sp 14.5 nm
Sv 14.3 nm
Sz 28.8 nm
ISO 25178Height Parameters
Sa 24.2 nm
Sq 29.3 nm
Sp 54.2 nm
Sv 73.4 nm
Sz 128 nm
ISO 25178Height Parameters
Sa 2.93 nm
Sq 4.51 nm
Sp 33.7 nm
Sv 6.56 nm
Sz 40.3 nmStamp bits including sidewall
Top surface of stamp bits
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Example Workflow for Pore Analysis nm
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Mean parameters on 545 grains
Number of grains: 545Total area occupied by the grains: 6.94 µm2 (48.5 %)Density of grains: 38.1 grains / µm2.
Area = 0.0127 µm2 +/- 0.166 µm2Perimeter = 749 nm +/- 9319 nmMean diameter = 68.3 nm +/- 24.8 nmMin diameter = 52.2 nm +/- 24.3 nmMax diameter = 97.1 nm +/- 52 nmForm factor = 1.07 +/- 1.4Aspect ratio = 2.88 +/- 4.31Roundness = 87.7 +/- 1996Orientation = 64.2° +/- 51.8°
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Mean diameter
1. Choose proper flattening method
2. Use height thresholding tool to select pits of interest
3. Binarization defines pores for 4. Display results
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Always remember…
When working with images, it’s good practice to:
1) Preserve raw data files before applying operators & filters
2) Keep a consistent workflow among data sets, especially when comparing statistical results
3) Try to avoid “over-processing” data and introducing artificial software image artifacts
