Automated Data Analysis for Event-Driven Scanning Transmission Electron Microscopy (Tempo-STEM)
Bibliographic record
Abstract
Recently [1], we introduced a method for improving the information-per-electron ratio in scanning transmission electron microscopy (STEM) based on pulse counting and event-driven beam blanking. This method, which we are calling “Tempo” (trigger-event modulated probability observation [2]) combines pulse counting, a custom logic circuit, and a fast beam blanker with arbitrary timing control (EDM or Electrostatic Dose Modulator) to listen to the STEM detector signals and blank the beam when a certain threshold is reached in each pixel. Tempo turns the usual measurement of STEM image intensity on its head. Rather than measuring the number of electrons detected in a fixed dwell time, Tempo measures the amount of time to reach a fixed number of detected electrons. When this threshold is reached it either immediately moves on to the next pixel or blanks the beam for the duration of the dwell time. This reduces dose, especially in high-scattering-rate sample regions, while also improving the information-per-electron efficiency by avoiding diminishing returns and equalizing the signal to noise ratio across the image. The process of calibrating and analyzing Tempo data is nontrivial. One must convert an analog pulse width modulation signal into a precise beam-on time in microseconds, then determine the ratio between that signal and a pulse-counted signal to determine either a detection rate (in counts per microsecond) or a mean detection interval (in microseconds per count). Various corrections must be made for time lags, statistical biases, and pixels where the threshold was not reached. This can be complex for an end user who would like to use Tempo without having to study the theory of the data analysis. To meet this need, we are introducing software that provides simple, real-time calibration and data analysis for Tempo measurements. The software integrates with the STEM control software (including planned plugins for Gatan’s Digital Micrograph[3] and JEOL’s FEMTUS) to provide a seamless interface for automatically generating output images that act like virtual detectors. Updates are done in real time, even as scans are in progress. The main interface window (Figure 1A) provides the essential features, with access to a few frequently accessed parameters and workflow controls, while an Advanced Settings window (Figure 1B) provides detailed, sophisticated low-level control when needed by expert users. This includes auto-calibration routines that determine zero offsets and scaling of the time axis. To the extent possible (depending on third-party constraints), the code will be made open source, allowing sophisticated users to fully understand the calibrations and add their own algorithms. The result is simple, directly interpretable images such as are shown in Figure 2. All input and output images, along with metadata fully documenting the calculations, are saved in standard file formats with consistent filename conventions allowing easy import into other analysis software. Example GUI windows for one version of the software. A. Main window used for general operation by all users. B. Advanced window with options that are set-once-and-forget for basic operation but enable fine control for unusual workflows. Example calibrated STEM images produced by the analysis. (Left) Beam exposure time in μs per pixel, using a maximum pixel dwell time of 12 μs. (Right) Dark field detection rate in counts per μs.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".