Precise and stable polarization control in a tightly focusing system for accurate characterization of strained Silicon nanostructures
Bibliographic record
Abstract
Recently, it was demonstrated that it is possible to excite and observe the “forbidden” TO phonons in ultrathin strained silicon (ε-Si) nanostructures using high-resolution polarized Raman spectroscopy [1]. While the allowed LO phonon observation is sufficient for isotropic strain characterizations, TO phonon is important for characterizing anisotropic strain relaxation, which is particularly present upon patterning nanostructures such as nanowires. Raman imaging of such ε-Si nanostructures requires very precise polarization control and highly stable focus positioning relative to the nanostructures within the focus. Moreover, as these structures become very small in size, a weaker signal is detected, thus needing longer exposure time at each position. Also, scanning over a large area with a number of nanostructures for better data sampling requires long duration experiments. In such situations, focus stability becomes a key concern due to the combination of thermal, vibrational and electrical noise, which compounds over time, limiting the spatial resolution in the submicron scale, hence worsening the contrast of the image.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".