Robotic Probing of Nanostructures inside Scanning Electron Microscopy
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
Probing nanometer-sized structures to evaluate the performance of integrated circuits (IC) for design verification and manufacturing quality monitoring demands precision nanomanipulation technologies. To minimize electron-induced damage and improve measurement accuracy, scanning electron microscopy (SEM) imaging parameters must be cautiously chosen to ensure low electron energy and dosage. This results in significant image noise and drift. This paper presents automated nanoprobing with a nanomanipulation system inside a standard 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 nanomanipulators 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 electron microscope 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) | 0.000 | 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