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Introduction to Image Processing and
A l iAnalysis
Gilbert Min Ph DGilbert Min, Ph.D.Applications ScientistNanotechnology Measurements DivisionMaterials Science Solutions Unit
Working with SPM Image Files
Raw data files (binary / ASCII formats)Limited tools for display & analysis
Realtime acquisitionRealtime acquisition
Post processing software68
136
Roundness
ISO 25178
0.2 0.3 0.4 0.5 0.6 0
Height ParametersSq 3.19Ssk -0.945Sku 3.85Sp 4.77
Results for presentation/publication
µm
-10
-8
-6
-4
-2
0
2
4
6
8
0 2 4 6 8 10 12 14 16 18 20 22 24 m m
Elem ent: segm ent o f width : 1 m m , Enclosed area : 0 .0653 m m 2
Agilent PicoImageResults for presentation/publication(.jpg, .tiff, .avi, .xls, etc.)
First Step: Image Leveling Most all SPM images require a basic leveling to remove inevitable artifactsMost all SPM images require a basic leveling to remove inevitable artifacts from image acquisition (sample tilt, scanner bow / nonlinearities, z-drift, line skips, etc.).
nm0 2 4 6 8 10 µm0
nm0 2 4 6 8 10 µm
0
350
400
450
500
550
6000
1
2
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4 120
140
160
1800
1
2
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4
100
150
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300
3505
6
7
8
9
40
60
80
1005
6
7
8
9
Original raw image After leveling process
0
50
µm
9
100
20
µm
9
10
Common approaches to leveling:- plane flattening- line by line flattening
Leveling Images: Plane FlattenSimplest approach – a linear plane is subtracted from surfaceSimplest approach a linear plane is subtracted from surface
nm
2527.53032.535
0 1 2 3 4 5 µm0
0.5
1
1.5
57.51012.51517.52022.52
2.5
3
3.5
4
4.5
02.5
µm5
nm
1314
0 1 2 3 4 5 µm0
0 5
LS plane fit
56789101112
0.5
1
1.5
2
2.5
3
01234
µm
3.5
4
4.5
Useful when there is very minimal curvature relative to the surface topography
Plane Flattening: 3-Point Method
Pl i i l d fi d b th d fi d f i t th fPlane is simply defined by three user-defined reference points on the surfaceUseful 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.
ZX1st order
The height average of each line is set equal to the previous line to remove any offset
ZX2nd order
ZX
X
3rd order X3rd order
scan linesleveled line
Line Flattening: a Cylindrical Hair Follicle0 2 4 6 8 10 µm
0
1
2
3
4
5
6
7
80th order (raw)
µm
9
10
0 2 4 6 8 10 µm0
1
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8
9
1st order
µm
10
0 2 4 6 8 10 µm0
1
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9
2nd order
µm
10
Using Include/Exclude with Line Flattening0 2 4 6 8 10 µm
0
1
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3
4
5
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7
8
9
µm10
1st order 1st order excluding raised stamps
0 2 4 6 8 10 µm0
1
2
3
4
0 2 4 6 8 10 µm0
1
2
3
4
Line by line levelled
5
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5
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10
Artifacts from line flattening can be avoided by identifying structures to include/exclude in the calculated polynomial used in subtraction
Line by line levelledµm µm
p y
2D / 3D Display OptionsColor Pallette
nm
300
350
400
450
500
550
6000 2 4 6 8 10 µm
0
1
2
3
4
5
nm
300
350
400
450
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550
600
0 2 4 6 8 10 µm0
1
2
3
4
5
0
50
100
150
200
250
300
µm
5
6
7
8
9
100
50
100
150
200
250
300
µm
6
7
8
9
10
Add Visualization Effects
3D continuous mesh 3D copper material
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.)
0 1 2 3 4 5 µm0
0.5
1
V
0.8
0.9
10 1 2 3 4 5 µm
0
0.5
1
1.5
2
2.5
3
3.5
4 0 2
0.3
0.4
0.5
0.6
0.7
0.8
1.5
2
2.5
3
3.5
4
+ =
µm
4
4.5
0
0.1
0.2
µm
4
4.5
5
surface potentialtopography 3D overlay
Organic materialPZT filmSDRAM Organic material phase overlaid on topography
PZT filmSP overlaid on topography
SDRAMSP overlaid on topography
Filtering: Removing Noise from Images Using a filtering algorithm can remove unwantedUsing a filtering algorithm can remove unwanted noise that often appears in acquired images
Matrix / Spatial FilteringMatrix / 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
1 2 1 0 0 0Types of Matrix Filters:
-Smoothing/denoising (median, mean, Gaussian)
-Min/Max
1 2 12 4 21 2 1
0 0 00 1 00 0 0
A “Custom” 3x3?3x3 Gaussian-Edge detection (Laplacian, Sobel, Gradient)
-Many more…including custom user-defined!
A “Custom” 3x3?
No effect:every pixel is
multiplied by 1
3x3 GaussianFilter
Applying Matrix / Spatial Filters0 1 2 3 4 5 µm 0 1 2 3 4 5 µm
0.5
1
1.5
2
0.5
1
1.5
2
median denoising7x7
2.5
3
3.5
4
4.5
2.5
3
3.5
4
4.5
µm5
0 1 2 3 4 5 µm
µm5
0 1 2 3 4 5 µm
0.5
1
median denoising27x27Sobel 7x7
edge detection0 1 2 3 4 5 µm
0.5
1
1.5
2
1
1.5
2
2.5
3
2.5
3
3.5
4
4.5µm
3.5
4
4.5
5
µm5
Fourier filtering
Filtering: Removing Noise from ImagesFourier 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 (1o line level)
2D FFT spectrum FFT filtered
Analysis Tools: Profile Extraction / Step Heightnm 1 2 3 4 5nm
100
150
200
250
300
Extracted profile
0 1 2 3 4 5 6 7 8 9 10 11 µm0
50
1 2 3 4 5
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
1 2 3 4 5nm
300
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
50
100
150
200
250
Extracted profi le
0 1 2 3 4 5 6 7 8 9 10 11 µm0
1 2 3 4 5
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 nmTotal 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
Measuring Surface Roughness Roughness parameters quantify height statistics of a surfaceRoughness parameters quantify height statistics of a surface
Some commonly reported values
Root Mean Square Standard deviation of the height distributionheight distribution
Arithmetic Mean Mean surface height
1st moment of distribution
Skewness 3rd statistical moment, qualifying the symmetry of
distribution
Kurtosis 4th statistical moment describing flatness of
EUR and ISO Standards exist for 2D & 3D describing flatness of
distribution
Maximum peak height Height between the highest peak and the mean plane
parameters to ensure conformity
Maximum pit height Depth between the mean plane and the deepest valley
Maximum height Heightbetween the highest peak and the deepest valley
Surface Roughness Examplesnm
14
15
16
17
18
19
20
21
nm
1.922.12.22.32.42.52.62.72.82.9
4
5
6
7
8
9
10
11
12
13
0 60.70.80.911.11.21.31.41.51.61.71.8
0
1
2
3
ISO 25178Height Parameters
00.10.20.30.40.50.6
ISO 25178Height Parameters “Smooth” film “Pitted” film Height Parameters
Sq 1.78 nmSsk -3.04Sku 18.4Sp 5.97 nmSv 15.3 nm
Height ParametersSq 0.268 nmSsk 0.0306Sku 3.08Sp 1.01 nmSv 1.92 nm
Sz 21.3 nmSa 1.06 nm
Sz 2.93 nmSa 0.214 nm
Surface Roughness: Same Surface, Different Scan Sizesnm
60
30
35
40
45
50
55
60
ISO 25178Height Parameters
Sq 10.5 nmSsk 0.408
5 um scan
0
5
10
15
20
25
30Sku 2.39Sp 35.7 nmSv 25.4 nmSz 61.1 nmSa 8.91 nm
0
nm
90
100
110
120
30
40
50
60
70
80 ISO 25178Height Parameters
Sq 8.38 nmSsk 0.74Sku 5.89Sp 96.8 nmSv 27.5 nm
0
10
20 Sz 124 nmSa 6.22 nm
Important calculations are made over appropriate length scales, as roughness
25 um scan
p pp p g gvalues depend on sample size
Using the Thresholding Toolf f ff / fAllows user to select surface planes of different altitudes/height levels for
manipulation
µm0 20 40 60 80 100 %
µ
0.119
0.139
0.159
0.178
0.1980 20 40 60 80 100 %
Place along curve corresponds to height level
0.0198
0.0397
0.0595
0.0793
0.0991
00 10 20 30 40 50 60 %
Abbott – Firestone Curve(height histogram & bearing ratio)
Using the Thresholding ToolSt b t t ISO 25178Stamp substrate ISO 25178
Height ParametersSa 2.94 nmSq 3.9 nmSp 14.5 nmSv 14.3 nmSz 28.8 nm
Top surface of stamp bits
ISO 25178Height Parameters
Sa 24.2 nmSq 29.3 nmSp 54.2 nmSv 73.4 nmSz 128 nm
ISO 25178Height Parameters
Sa 2.93 nmSq 4.51 nmSp 33.7 nmSv 6.56 nmSz 40.3 nm
Stamp bits including sidewallsidewall
Example Workflow for Pore Analysis nm
18.8
29.5
40.3
51
61.7
0 20 40 60 80 100 %0 0.25 0.5 0.75 1 1.25 1.5 1.75 2 2.25 2.5 2.75 3 3.25 3.5 3.75 µm0
0.25
0.5
0.75
1
1.25
1.5
-34.8
-24.1
-13.4
-2.63
8.09
0 2.5 5 7.5 10 12.5 15 17.5 20 %
1.75
2
2.25
2.5
2.75
3
3.25
3.5 Thresholded -
208
Form factor
µm
3.75
0 1 2 3
1. Choose proper flattening method
2. Use height thresholding tool to select pits of interest
1 2 3 4 5 6 0
104
0 1 2 3 µm0
0.5
1
1.5
2
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 nm Mean diameter
µm
2
2.5
3
3.5
Perimeter = 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° 70.5
141
Mean diameter
3 Binarization defines pores for 4 Display results
0 1 2 3 µm0
0.5
1
1.5
2
2.5
3
20 40 60 80 100 120 140 nm0
3. Binarization defines pores for 4. Display resultsµm
3.5
Always remember…
When working with images, it’s good practice to:
1) Preserve raw data files before applying operators &1) Preserve raw data files before applying operators & filters
2) Keep a consistent workflow among data sets2) Keep a consistent workflow among data sets, especially when comparing statistical results
3) Try to avoid “over-processing” data and introducing3) Try to avoid over processing data and introducing artificial software image artifacts