High-Resolution EELS in an aberration-corrected LEEM: Design of electrostatic transfer lenses for hemispherical filters
Bibliographic record
Abstract
Recent advances in Low-Energy Electron Microscopy (LEEM), including the development of aberration-corrected (AC) systems, have significantly enhanced spatial resolution. However, further progress is limited by the energy resolution of current instruments. In this work, we propose a novel approach to address this limitation by integrating two Hemispherical Deflector Analyzers (HDAs) in tandem: the first serving to monochromatize the electron source, and the second to enhance the spectroscopic performance of AC-LEEM for Electron Energy Loss Spectroscopy (EELS). This dual-HDA configuration provides a clear pathway toward combining high spatial and energy resolution, expanding the capabilities of LEEM for advanced surface and materials characterization. This paper discusses various criteria for implementing these HDAs on a commercial AC-LEEM and presents more specifically the design of four transfer lenses (TLs) for electron transfer between the HDAs and the other optical components of the instrument. The use of a natural aberration correction scheme based on the dispersion-compensation (DC) principle is also discussed for maximum current throughput. Using ray-tracing simulations, we first show that the TL design can effectively decelerate/accelerate the electrons between 0.1 and 15 keV, thus respecting the high-voltage operation of the AC-LEEM. A double focus of the electron beam is also simulated so that the electron positions are conserved after transfer at the exit/entrance of the HDAs, an important condition for DC operation. Finally, ray-tracing simulations of the TLs show that the focal plane can be switched from the image plane to the back focal plane, allowing fast switching between diffraction and imaging modes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".