Electron Microscopy: Ready to Use AI for Focus and Astigmatism Adjustment

• Ready to implement technology
• Onetime training for specific environment
• Flexible application to various types, models and
  imaging parameters of electron microscopy
• Low processing time
• Quick convergence
• Applicable also for high-throughput electron
  microscopes
• Scalable accuracy by variation of input data
  amount
• Simple to use

• Scanning electron microscopy
• High electron dose imaging
• Automated (3D) imaging
• High precision microscopy with large samples

With increasing resolution and sample sizes the auto focusing method is significant for the result of automated electron microscopy. State of the art methods rely on the estimation of wavefront aberrations caused by variations of the relevant settings, hence being microscope specific and involving detailed theory. The lack of applicability to different systems and samples as well us long processing durations make existing solutions insufficient.

A new auto focusing method based on deep learning has been developed to overcome the aforementioned shortcomings. It was developed and tested with a typical imaging scenario involving the adjustment of the working distance and astigmatism corrections in two dimensions, as shown in figure 1. The basic optimization procedure is visualized in figure 2 for this exemplary context. Two images (2, 3) with known parameter perturbations (4, 5) around the current setting for an flawed image (6) are used as an input. The method according to the invention selects subareas (7, 9) of the first image and identical sections (8, 10) from the second one. The processing unit (11) uses each image patch pair as an input to estimate a correction term (12) to the current working distance and stigmator setting, which are entered to a function calculating the final correction suggestion. The processing unit (11) should at least comprise one processor (13) and a memory (14). After applying the obtained settings, an optimized image (15) can be taken.
The AI can be trained for various sorts of microscopes and is also capable of optimizing other parameters than those presented. It was shown that for a standard initial out-of-focus setting the optimization quickly converges and 3 iterations of the described procedure are sufficient for determining ideal settings.

(Application Filed)