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Record W4412910686 · doi:10.1093/mam/ozaf048.1127

Segmented Detector Digitization and the Role of Denoising for Increasing Achievable Temporal Resolution in Phase Characterization

2025· article· en· W4412910686 on OpenAlexaff
Julie Marie Bekkevold, Taichi Kusumi, Georgios Varnavides, Jonathan J. P. Peters, Ryou Ishikawa, N. Shibata, Lewys Jones

Bibliographic record

VenueMicroscopy and Microanalysis · 2025
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsTrinity College
Fundersnot available
KeywordsDigitizationCharacterization (materials science)Noise reductionDetectorResolution (logic)Materials sciencePhase (matter)Computer scienceArtificial intelligenceOpticsPhysicsComputer visionNanotechnology

Abstract

fetched live from OpenAlex

Phase contrast and retrieval methods have become increasingly important for characterization of materials in scanning transmission electron microscopy (STEM) in recent years. Since physical detectors only detect the intensity of the electron wave function, all phase information in the transmitted electron beam is lost. Phase contrast and retrieval methods aim to recover the phase information from the electron beam by recording the convergent beam electron diffraction (CBED) pattern in the bright field region, using pixelated or segmented detectors. Pixelated detectors capture fine details in the CBED pattern by capturing an image of the pattern at every scan position, creating a 4D dataset. This technique, known as 4D-STEM, has been shown extensively to produce high quality phase reconstructions through many phase retrieval methods including integrated center of mass (iCOM) and iterative ptychography [1-4]. However, 4D-STEM datasets tend to be massive (multiple GB), and hamper the practicable scan speed due to a high read-out overhead of the CBED pattern image at each scan position. As a result, the achievable temporal resolution with pixelated detectors is limited. Achieving high temporal resolution requires implementation of very fast scan coils, and use of a segmented detector with minimal read-out overhead. Still, all detectors have a finite response time for each electron detection event, and when the dwell time reaches or goes below this response time that causes streaking artefacts in the image [5]. These artefacts appear because signal for a single electron event is recorded in multiple subsequent pixels [6], but may be removed by live, in-hardware digitization of the detector signal [7]. This digitization method thresholds the gradient of the detector signal to pick out electron detection events and passes the digitized image back to the image recording software. This removes the analog noise present in the detector, giving a digital signal with a true-zero noise-floor, and ensure that each electron detected is a digital one and thus reducing the impact of detector surface inhomogeneity. In this work, we combine the use of ultra-fast scan coils [8] capable of achieving dwell times down to a few tens of nanoseconds, with digitization of the signal from a segmented annular all-field (SAAF) [9] scintillator-based detector. Working with digital signals opens the possibility of using denoising algorithms specifically designed to work with the Poisson statistics of electron detection [10]. The raw COM image in Fig. 1. shows how noisy the data is before denoising when acquired using beam current 1.9pA and dwell time 200 ns; the semi-convergence angle was 30 mrad. Even when summing 21 frames, no atomic columns are visible to the human eye in the raw COM image, whereas in the Poisson denoised image in Fig. 1. the atomic columns become visible. As a result of the denoising, the quality of the iCOM reconstructions is also improved, see Fig. 2. Here, we show how the use of this denoising framework helps boost the achievable temporal resolution for low-dose characterization of STO. Further, we compare this with the reconstructions achievable using iterative ptychography for data from this detector with only four segments and compare how the inherent denoising of the iterative ptychography algorithm compares with the explicit denoising for iCOM phase retrieval [11]. Comparison of the COM for a sum of 21 consecutive digitized frames from four segments on a segmented annular all-field detector, showing how noisy the raw signals are. The Poisson denoising helps make the atomic columns visible. iCOM reconstructions from the sum of 7, 14, and 21 frames. Note how Poisson denoising of the segment signals makes the atomic columns more easily visible than in the reconstructions from raw data.

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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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.003
GPT teacher head0.229
Teacher spread0.226 · 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 designSimulation or modeling
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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