Automated nanoprobing under scanning electron microscopy
Why this work is in the frame
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Bibliographic record
Abstract
Nanomanipulation inside electron microscopes enables a multitude of precision applications. The semiconductor industry employs this capability to probe sub-micrometer-sized features to evaluate the performance of integrated circuits (IC) for design/quality monitoring. In electron microscopy imaging, the use of low accelerating voltages and high magnifications, as required for IC nanoprobing tasks, results in significant image noise and drift. This paper presents automated nanoprobing with a nanomanipulation system inside a standard scanning electron microscope (SEM). We achieved SEM image denoising and drift compensation in real time. This capability is necessary for achieving robust visual tracking and servo control of nanoprobes for probing nanostructures in automated operation. This capability also proves highly useful to conventional manual operation by rendering real-time SEM images that have little noise and drift. The automated system probed nanostructures on an SEM metrology chip as surrogates of electronic features on IC chips. Success rates in visual tracking and Z-contact detection under various imaging conditions were quantitatively discussed. The experimental results demonstrate the system's capability for automated probing of nanostructures under IC-chip-probing relevant EM imaging conditions.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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