Dear Sir, Thank you for the video. I have one question. If flattening is because of sample tilting and scanner bowing, then during potential mapping (like in KPFM), this problem can also arise. So, is it possible to flatten the potential image in KPFM? Will the potential be correct then?
Hi Rohit! Thank you for the question. Theoretically, if tip-sample distance is controlled accurately during the second pass -- KPFM map should not get influence from the tilt or bow. Essential point here is good quality of topography on the first pass, of course as well as usage Z sensor for the second pass. But practically KPFM maps may get artifacts because of not ideal topography or due to tip properties change. E.g. tip gets hit with some feature and can either loose piece of coating or catch some particle. In this case we can't use this data as quantitative result, but in the same time it may still be illustrating our surface qualitatively. We would suggest to use 0 order subtraction in latter case.
Dear Sir, Thank you for the video. I have one question. If flattening is because of sample tilting and scanner bowing, then during potential mapping (like in KPFM), this problem can also arise. So, is it possible to flatten the potential image in KPFM? Will the potential be correct then?
Hi Rohit! Thank you for the question.
Theoretically, if tip-sample distance is controlled accurately during the second pass -- KPFM map should not get influence from the tilt or bow. Essential point here is good quality of topography on the first pass, of course as well as usage Z sensor for the second pass.
But practically KPFM maps may get artifacts because of not ideal topography or due to tip properties change. E.g. tip gets hit with some feature and can either loose piece of coating or catch some particle. In this case we can't use this data as quantitative result, but in the same time it may still be illustrating our surface qualitatively. We would suggest to use 0 order subtraction in latter case.