EDAX Inc., Ametek, 91 McKee Place, Mahwah, NJ 07430 USA. 2. EDAX Tilburg, PO. Box 4144, 5004JC Tilburg, The Netherlands. Post Processing Methods.
Scientific Analysis of NPAR™ Processing on EBSD Results 1 Camus ,
1 Wright ,
1 Nowell ,
2 Kloe
Patrick P. Stuart I. Matthew M. Rene de 1EDAX Inc., Ametek, 91 McKee Place, Mahwah, NJ 07430 USA 2EDAX Tilburg, PO. Box 4144, 5004JC Tilburg, The Netherlands Motivation
Investigation of Pixel Changes
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Emphasis of modern EBSD systems is often on speed – –
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Other factors that lead to poor pattern quality: –
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Low BSE generation
Low sample perfection •
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Low BSE generation and low phosphor efficiency
Low beam current for smallest spot size
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186x150 square pixel OIM maps collected ~10nA at 20kV Camera binned to 60x60 pixels @ ~ 1000 fps Exposure time of 0.9 ms (gain adjusted) Specimen courtesy of E. Griesshaber, Geo- und Umweltwisschaften, Ludwig-Maximilians-Universität München Highly insulating and easily damaged by electron beam 1130x916 square pixel OIM maps collected
Diffraction patterns saved
Internal defects degrade diffraction
Post processing methods are used to increase the data quality
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Compare maps, histogram and pixel metrics before and after processing – – –
How is the data being changed on a pixel basis? •
IQ, CI, IPF, KAM PF, IPF IQ, CI, Fit, KAM, delta IPF
There is an obvious increase in the feature quality when using NPAR, but the original patterns are quite good without processing
Original
NPAR
Kernel Operations – – – – –
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Pixel data replacement with median or mode value of nearest neighbors Fast processing operation Repeated as needed to fill data gaps Produces visually appealing final maps Can be abused for low quality maps
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Pixel re-indexing using averaged pattern from nearest neighbors Slower processing operation, but only takes a few minutes Uses traditional indexing routine Single operation Produces high quality results which typically do not need further processing, but could be performed
Original
NPAR
NO additional post processing applied to data
Summary • • •
NPAR –
• Boundaries in the IQ maps by NPAR are better defined. • NPAR is a kernel operation, so very small features like thin twins disappear. • Less noise pixilation in NPAR which might be expected by a kernel operation, but the kernel was applied to diffraction patterns before IQ calculation NOT to map pixel data.
• The IPF map starts with only reasonable quality and is significantly enhanced with NPAR processing, but is not perfect. • It should be remembered that the kernel pattern averaging of the NPAR routine produces a pattern quality that would require ~8x acquisition time.
• A simple comparison is to plot the IQ values of each pixel before and after the NPAR routine. • As expected, a vast majority of the pixels increased in IQ by using the routine • However, a significant number DECREASED by using the routine, which was highly unexpected.
Post Processing Methods •
NPAR
NPAR
Brachiopod Shell (CaCO3) –
Low voltage for smallest interaction volume •
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Original
Original
Annealed Nickel – – – –
e.g. Hikari Super (>1400 indexed pps) But short exposures may lead to poor pattern quality.
Original
NPAR
Specimen courtesy of E. Griesshaber, Geo- und Umweltwisschaften, Ludwig-Maximilians-Universität München
NPAR processes stored EBSD patterns to increase the quality of EBSD data NPAR is more time effective than increasing camera exposure NPAR provides higher quality orientation data with traditional indexing routines –
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Pixels with the lowest acquired quality patterns are enhanced the most
NPAR, being a kernel operator, loses very thin grains and twins, and changes local orientations – –
Pixel spacing for maps should be considered Not recommended for strain analyses
Conclusion •
NPAR is powerful routine for automatically and objectively increasing the quality of EBSD data.
Nickel Degraded IQ Locations • When those IQ-degraded pixels are shown on the IQ map, they are all located on boundaries. • It is reasoned that the kernel operation is averaging more “difficult” boundary patterns that are not enhancing the indexing operation but actually hurting the analysis. • This indicates that an algorithmic change should be considered for boundary pixels.
IPF maps show that NPAR operation reduces the noise associated with boundary edges but also “removes” very thin twins and grains.
• A delta-IPF map illustrates the locations where the data has changed the most: thin twins, grain boundaries, and high-strain pixels. • A plot of the histogram of the delta-IPF map
Original
• The IQ maps do not significantly increase in quality although the contrast is enhanced slightly. • The kernel operation of the NPAR routine increases the S/N in the patterns.
Original
NPAR
NPAR
– Not shown are a few pixels with orientation changes as high as 14 degrees, probably associated with the “lost” twins
• The vast majority of pixels show orientation changes well less than 1 degree. – Although these values are very low, NPAR is not recommended for strain analyses studies as the operation will modify the strain value by this amount.
The NPAR processing does not significantly change the PF and IPF texture plots but simply concentrates the existing intensities. With original map data of high quality, the IPF histograms show very little if any changes when NPAR processed.
