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Record W4412909022 · doi:10.1093/mam/ozaf048.915

Towards Cinematic STEM and Beyond: Fast Frame Rates Using Overdriven Scan Shaping

2025· article· en· W4412909022 on OpenAlexaff
Jonathan J. P. Peters, Grigore Moldovan, Lewys Jones

Bibliographic record

VenueMicroscopy and Microanalysis · 2025
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Image Processing Techniques
Canadian institutionsTrinity College
Fundersnot available
KeywordsFrame (networking)Materials scienceComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

Faster frame rate imaging in scanning transmission electron microscopy (STEM) has long been desired to mitigate the effects of specimen drift [1], capture dynamic events [2], or for controlling electron dose [3]. Until recently, conventional STEM imaging synced each line with the mains electrical frequency to minimize the effects of external fields. However, this limits frame rates to ∼0.1 frames per second (fps) for typical image sizes (512x512 pixels). In more recent years, multi-frame imaging has been employed with frame rates on the order of 2 fps, where distortions in the stack of frames can be diagnosed and removed [1]. One of the major roadblocks to faster scanning is the requirement for a flyback time between scan lines, allowing time for the beam to travel from the right to the left of the image. As dwell-times are reduced more and more, the fixed flyback time represents a larger and larger percentage of the overall total acquisition time and dose. This flyback hysteresis can be reduced using computational post-processing [4], but further frame rate improvements require new hardware. The new generation of scan controllers are capable of arbitrary scans, allowing the possibility of new scanning strategies [5-7] for reducing flyback or reducing the number of exposed pixels. Pixel times on the order of 10 ns are also now achievable, though the deflection systems in most STEMs are not capable of such speeds. This is from a combination of the inductive nature of the scan coils, and the limitations of the electronics controlling them. New coil designs can reduce scan coil inductance [2], though with a limited field of view and not widely available on all microscopes. To achieve the highest frame rates on existing STEMs and at low magnifications, a method to mitigate the effects of the deflection system whilst scanning near the maximum speed of the scan controller is needed. For this goal, we propose the serpentine scanning pattern where alternating lines are scanned in opposite directions. This removes the large discontinuities in beam position of conventional scanning and has a minimal number of discontinuities in the beam velocity compared to other scanning patterns (e.g. Hilbert scans). Despite this, a fast serpentine scanning pattern will still suffer due to the imperfect deflection system as the beam cannot change direction instantaneously. To account for this, we propose an overdrive system to shape the scan to the desired output, in this case a modified serpentine with deceleration at the line ends. By overdriving the beam (i.e. programming the beam to go to a further position) we can force the beam to be in the desired position. We measure this using a CCD camera to directly measure the beam position in a confocal configuration (Fig. 1), and apply an iterative correction to account for the non-linear response of the beam (Fig. 2). We demonstrate this on a Thermo Fisher Titan G2 300 kV equipped with a point electronic REVOLON scan controller and Gatan UltraScan 1000 CCD. Using our method we can move towards imaging at ∼50 ns per pixel, and frame rates of 50 fps with fully sampled images. We also discuss the current problems with fast scanning and paths towards even greater frame rates in the future [8]. Example measured beam positions following a serpentine with 1024 STEM points (pixels) per period (equivalent to a serpentine scan with 512 left-to-right and 512 right-to-left pixels). Scanning speeds are 1 μs per pixel (blue), 200 ns per pixel (green), 100 ns per pixel (yellow), and 50 ns per pixel (red). As the scan becomes faster, it deviates more and more from the ideal triangular wave. Pre, (a), and post, (b), corrected deviation of measured beam position (dashed black) from desired beam position (solid orange).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.002

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.

Opus teacher head0.017
GPT teacher head0.315
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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".

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Citations0
Published2025
Admission routes1
Has abstractno

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