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Record W4385073314 · doi:10.1093/micmic/ozad067.229

Characterization of the Performance of a Thin Si-based Timepix3 Detector at 10–30 keV Electron Energies

2023· article· en· W4385073314 on OpenAlexaff
Tianbi Zhang, Kirsty A. Paton, T. Ben Britton

Bibliographic record

VenueMicroscopy and Microanalysis · 2023
Typearticle
Languageen
FieldMaterials Science
TopicElectron and X-Ray Spectroscopy Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCharacterization (materials science)DetectorMaterials scienceElectronNuclear physicsAtomic physicsPhysicsNanotechnologyOptics

Abstract

fetched live from OpenAlex

Direct electron detectors such as hybrid pixel array detectors based on the Timepix3 chip [1] have received increasing attention within the electron microscopy community due to their high signal-to-noise ratio and dynamic range as compared to conventional indirect detectors, as well as their ability to perform energy filtering [2]. Earlier applications of these direct electron detectors have focused on techniques based in transmission electron microscope (TEM). More recently, the use of direct detectors has also been probed in lower electron energy applications, especially those based in scanning electron microscope (SEM), such as electron backscatter diffraction [3]. Lower incident electron energies will reduce the depth at which electron energy is deposited, and therefore enable the potential use of thinner sensing layers. Reduced diffusion of signal-carriers produced by incident electrons in thinner sensors should improve the imaging performance compared with thicker sensors. To assess the value of these imaging detectors, the detector performance can be quantified through analysis of the modulation transfer function (MTF) and detective quantum efficiency (DQE). The MTF measures the transfer of contrast as a function of spatial frequency, and the DQE is an overall measure of the effective transfer of signal. While many previous works have measured the MTF and DQE at electron energies typical for TEMs (e.g. 60–200 keV), there is limited information available of detector performance at the lower energies used in SEM-based microscopy (up to 35 keV). In this work, we characterize a Timepix3-based direct electron detector with a 100-μm thick Si sensing layer at lower (10–30 keV) electron energies. A new experimental protocol to facilitate the measurements which can be conducted in SEM is proposed. Measurements of MTF and DQE were performed in a field-emission gun SEM using the knife edge method and a flat field image method respectively [4]. The MTF was found to decrease with higher electron energy, which agrees with the trend based on previous literature, and shows improvement of MTF from higher electron energies and thicker sensing layers [5–6]. Measurements of the DQE, currently ongoing, will confirm the extent to which a thin sensor improves the device's ability to resolve features of different sizes when the total number of electrons contributing to image formation is limited. Our measurements have found that thin hybrid pixel array detectors provide improved electron detection and imaging in SEMs (including operation in scanning electron microscopy-based transmission modes) for electron energies between 10 and 30 keV. This data can support and motivate the wider-adoption of these direct detectors for electron diffraction and related SEM-based microstructural analysis methods [7].

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.735

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.240
Teacher spread0.235 · 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 teacher head, not a consensus.

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

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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