STM Scan-distortion Compensation

Correction of scanning tunnelling microscope tip-height and stage instabilities for improved resolution and SNR.

STM Scan Correction

Scanning tunneling microscope image of strontium titanate before (left) and after (right) tip-height and scanning-distortion correction. Data credit: Matthew Marshall (click to enlarge).

Scanning tunnelling microscope images, like all scanned data, can suffer from environmental distortions. These manifest as tip-height errors giving bright or dark bands through the image, or as lateral scanning-distortions  bending and shearing image features. These distortions are similar to those in STEM and can be corrected in the same way.

Using the Smart Align software, images with high signal-to-noise ratio (SNR) and excellent resolution (better than 2Å) can be produced.

STM Data Processing with the Smart Align Software

The videos below show the data-processing stages in a typical scanning tunnelling microscope data-set. In this data-set the scanning orientation (fast-scan direction) is fixed with respect to the sample (always horizontal). The same steps can be used for atomic force microscope data.

Raw STM Time-series Movie:

First we begin by recording a multi-frame STM data-set (a time-series movie). Here we mainly see the effect of sample-drift.

After Rigid-registration:

After correcting sample-drift (translation) the next most striking feature here becomes the tip-height errors observable as brightness fluctuations.

After Tip-height Correction:

Once the tip-height errors are corrected the dominant distortion that becomes visible is the non-linear scanning distortion.

After Non-rigid Registration (scan-distortion correction):

Non-rigid registration is used to correct the non-linear scan-distortion. Once all these corrections are complete, the true sample dynamics are far easier to interpret.

These corrections were performed using the Smart Align software; further details can be found here. Further details about the methods used and the sample details can be found here.

This work was recently featured in Oxford University’s innovation magazine “Isis Insights“.

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