<b>2014</b><i><b>Microscopy Today</b></i><b>Innovation Awards</b>
Bibliographic record
Abstract
Topspin Strain Mapping software employs electron diffraction and electron beam precession technology to measure strain in local regions of thin specimens.The software enables the user to acquire STEM images and interactively select two areas of interest for strain measurement:(1) a reference, ideally unstrained, region and (2) an experimental, strained region.Electron diffraction patterns from the two regions are compared by the Topspin Strain Analysis software to measure strain values at every sampled position in the experimental area.Unlike other methods, which calculate strain by measuring shifts in individual diffraction spots from the patterns, Topspin Strain Analysis calculates strain by comparing the entire strained diffraction pattern to the unstrained reference pattern.This improves the accuracy and precision of the strain measurement and does not require the user to identify the spots to use for strain measurement.The addition of beam precession to the nanobeam diffraction patterns reduces the effects of specimen thickness and tilt variations, which arise from dynamical diffraction effects.This enables the robust use of automated analysis algorithms.Additionally, the increased number of high-order diffraction spots in the precession electron diffraction patterns improves the precision of the strain measurement.Topspin Strain Mapping is an automated solution for research and metrology applications that require strain determination and mapping, and it yields both high measurement precision (0.02% demonstrated) and high spatial resolution (nanometer-level demonstrated).Topspin Strain Mapping addresses the issue of low throughput typical of previous methods.Through the use of hardware optimized for fast diffraction pattern imaging and beam precession, a typical Topspin Strain Mapping session acquires a dataset of 200 × 200 pixels in less than seven minutes.Subsequent analysis of the dataset from such a map can produce strain maps in approximately five minutes.Additionally, there is little user input necessary for analysis, which reduces complexity and improves reproducibility.Strain measurement is important for layered semiconducting materials where interfacial strain on electronic mobilities and properties can affect device performance.The high throughput, minimal user input, and accuracy gained by taking measurements from the entire diffraction pattern make the technique suitable for industrial applications, as well as local measurement of strain around crystal defects and precipitates that are important for fundamental applications in materials science.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.321 | 0.079 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".