Optimizing SEM parameters for segmentation with AI – Part 1: Generating a training set
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Extracting significant quantitative results from SEM images requires feature segmentation with image processing software. The efficiency of segmentation algorithms depends on the image quality, determined by the parameters set on the microscope during acquisitions. By integrating AI within SEM acquisition workflows, it is possible to suggest microscope parameters that will produce images where the features to quantify will be easily segmented. Specifically, a model is trained to automatically suggest the beam energy and probe current to set on the microscope during acquisitions. This paper is the first of two parts, describing workflows for generating a complete training set. The training set is carefully designed, consisting of both simulated data and real data acquired on the SEM by varying the energy and current. Separate workflows are required for generating simulated and acquired training examples. Simulated data generation is accomplished with the MC X-ray simulator in Dragonfly, where multiple virtual samples are created to intentionally diversify the training set. Acquiring data on the SEM for training is a time-consuming process when compared to generating simulations and would ideally be avoided but is included here to determine the degree to which it is required. Using only simulated data for adequate training, we show that our data generation workflow can be fully automated and produces a considerable amount of high quality data rapidly and with minimal effort.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it