Silicon nanostructures with very large negatively tapered profile by inductively coupled plasma-RIE
Bibliographic record
Abstract
Dry etching of silicon has been extensively studied, mostly with a goal of obtaining perfectly vertical sidewalls with high aspect ratio. Yet, sloped sidewall with a negative taper angle (i.e., diameter/width decreases linearly with depth) may find various applications. However, the systematic study on the etching process development to obtain such a profile is rather scarce. In this work, the authors present a controlled and reproducible fabrication process to achieve silicon nanostructures with negatively tapered sidewall profile using inductively coupled plasma-reactive ion etching with C4F8 and SF6 gas. The plasma etching parameters have been thoroughly optimized in order to avoid the undercut or curved reentrant profile due to isotropic etching, so as to achieve a negatively tapered profile. The influence of the plasma etching parameters, especially the radio freguency power and C4F8/SF6 gas flow ratio, on the etching rate and the sidewall taper angle has been analyzed. With an optimal etching recipe, the silicon nanostructures with an unprecedented large 10° negative taper angle were achieved. These results were demonstrated on different structure sizes of 500 nm, 700 nm, and 1.2 μm diameters.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 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".