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Record W4401006433 · doi:10.1093/mam/ozae044.704

Streak Imaging in a Dynamic Transmission Electron Microscope

2024· article· en· W4401006433 on OpenAlexaff
Kenneth R. Beyerlein, Samik Roy Moulik, Yingming Lai, Aida Amini, Patrick Soucy, Jinyang Liang

Bibliographic record

VenueMicroscopy and Microanalysis · 2024
Typearticle
Languageen
FieldMaterials Science
TopicElectron and X-Ray Spectroscopy Techniques
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsStreakStreak cameraElectron microscopeTransmission electron microscopyMaterials scienceScanning transmission electron microscopyConventional transmission electron microscopeOpticsElectron tomographyTransmission (telecommunications)MicroscopeNanotechnologyComputer sciencePhysicsTelecommunications

Abstract

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A suite of high-speed electron microscopy instruments and methods have been developed in the past few decades to watch nanomaterial excitation and dynamics in real time [1-3]. The most widespread is the ultrafast transmission electron microscope (UTEM), which has been used to study light-induced excited states of materials with femtosecond time resolution. As a single pulse does not contain sufficient electrons to form an image, a UTEM image is collected stroboscopically, and capturing one single frame can require an exposure of more than a minute [1]. A time series of 20 frames can then take 30 minutes to collect, requiring stable microscope operation, no sample degradation, and its reversible response on this time scale. Alternatively, the dynamic transmission electron microscope (DTEM) has been developed for snapshot imaging of transient and irreversible processes such as phase transformations. The DTEM relies on the generation of high-charge electron pulses (∼107–108 electrons per pulse) to form an image in a single shot [4,5], while the movie-mode DTEM (MM-DTEM) can film a transient event utilizing a train of such pulses [6]. At this high-charge limit, electron-electron interactions play a significant role in defining many properties of the pulses. For example, the maximum photoemitted current density is governed by the Child-Langmuir limit [7,8], the pulse energy distribution is broadened by the Boersch effect [1], and the pulse also suffers from longitudinal and transverse broadening at beam crossovers as it propagates in the column [1]. Despite all these effects, no measurements of the temporal profile of high-charge electron pulses in a DTEM have been previously reported. This work presents our recent progress in implementing different modes of streak imaging functionality in a MM-DTEM, and its use to characterize the spatio-temporal evolution of the generated high-charge electron pulses. Notably, we will also demonstrate the application of tomographic compressed sensing image reconstruction to recover a sequence of two-dimensional images of a 1.85-µm-diameter field of view (FOV) with nanoscale spatial resolution, 370-ps inter-frame interval, and 140-frame sequence depth in a 50-ns time window. This new functionality has the possibility to rapidly collect sets of images with picosecond time resolution, relieving the constraint on sample stability, and allowing for new in situ experiments that follow the ultrafast material response as it evolves. The movie-mode DTEM at INRS is comprised of an IDES® cathode laser system and a modified JEOL® JEM 2100-Plus TEM [6]. The microscope contains electrostatic beam deflectors positioned downstream of the image forming lenses, which in the movie-mode function, applies a static voltage as the electron pulse is traveling through a pair of metal plates to deflect the image onto different regions of the camera. We realized streak-mode DTEM (SM-DTEM) functionality by installing a custom deflector voltage controller (Axis Photonique®) to apply a voltage ramp to the plates synchronized with the electron pulse. This formed a streak image on the camera that separated the electron arrival time into different positions on the camera along the streak direction. The most straight forward analysis for this kind of measurement consists of extracting a linear trace along the streak direction to obtain the average transmitted intensity at different times during the pulse. We consider this a zero-dimensional (0D) SM-DTEM measurement, as it yields the evolution of the average transmitted electron intensity over a FOV defined by a selected area aperture (SA) and used it to characterize the temporal profile of the photoemitted electron pulse. Figure 1 shows a set of 0D SM-DTEM traces collected as a function of cathode laser pulse energy with the beam centered in a 100-μm SA and a sweep rate of 4 V/ns, corresponding to an estimated time resolution of 7 ns. It is seen that at high pulse energy, there is a peak in the photoemission at the beginning of the electron pulse. As the pulse energy is decreased, this peak gradually shifts from 18 ns to 10 ns. However, it is found to persist to a UV pulse energy of 0.03 mJ, well below the photoemission saturation limit. We will present this result cross-referenced with other complementary measurements, to show that this peak in the photoemission profile does not follow the cathode laser pulse and is believed to be caused by anomalies in the photoemission process. Then, we will introduce two-dimensional (2D) SM-DTEM imaging, which consists of applying the principles of compressed ultrafast tomographic imaging to recover a time sequence of images from a set of streak images [9,10]. This can be conceptualized by considering a streak image as an overlapping set of images, where the degree of overlap is determined by the sweep rate of the electron beam on the camera. Then by capturing multiple streak images with different sweep rates and sweep directions, redundant information is obtained about the scene, which is analogous to viewing an object at different orientations in tomographic imaging. We will then explain how compressed sensing tomographic image reconstruction algorithms can be applied to this type of data to recover the sequence of images making up the scene. To demonstrate this and study the recovered image quality, we conducted a series of measurements of the electron pulse passing through a Ted Pella gold cross-grating sample. A set of streak images collected in different directions and sweep speeds was acquired for a 1.85-μm-diameter 2D FOV of the sample. This was then input into a two-step iterative shrinkage/thresholding (TwIST) algorithm-based tomographic reconstruction (TTR) algorithm [9,10] to recover a set of 2D images (Fig. 2). The TTR algorithm was able to recover the scene of the electron beam passing through the sample with an imaging speed of ∼2.7 billion fps (i.e., a 370-ps inter-frame interval) and a sequence depth of 140 frames. It should be noted that this inter-frame interval provides more than an order of magnitude improvement over the 7-ns time resolution of 0D SM-DTEM measurements shown in Fig. 1. The high quality of the reconstructed images is seen in the selection of frames shown in Fig. 2. Notably, the contrast of the square grid grating is clearly resolved, as are latex spheres decorating the surface, which are seen as dark circles near the right edge of the FOV. We will then present further quantitative analysis of the recovered image quality and spatio-temporal characterization of the transmitted electron pulse. In summary, the SM-DTEM developed at INRS has been used to study the photoemission process of high charge photoemitted electron pulses. Furthermore, we demonstrate the use of compressed ultrafast tomographic imaging to recover a sequence of 2D images with picosecond time resolution from a set of streak images. This is new functionality complements other high-speed electron microscopy approaches and can enable new views into the dynamics of nanomaterials. 0D SM-DTEM traces of 50-ns photoemitted electron pulses measured for different UV cathode laser pulse energy. Selected frames of 2D SM-DTEM reconstructed scene of 50-ns electron beam passing through gold cross grating sample. Scale bar = 500 nm.

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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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0150.003

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 head0.004
GPT teacher head0.280
Teacher spread0.276 · 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
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".

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Citations0
Published2024
Admission routes1
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