Cryogenic shallow reactive ion etch process for profile control on silicon on insulator platform
Bibliographic record
Abstract
A cryogenic reactive ion etch (RIE) process is presented to fabricate shallow two-dimensional photonic crystal type dense pattern microstructures (usually with thickness less than 500 nm and with low aspect ratios ∼1–4) on a silicon on insulator (SOI) platform. Deep RIE etching of silicon has been previously investigated particularly with respect to etch rate, etch profile, and selectivity. While using an oxide layer as an etch stop has also been investigated, the profile control near the oxide interface is usually not very important due to the large aspect ratios. However, for shallow structures with low aspect ratios, profile control near the oxide interface is important while the etch rate and the selectivity are not as much of a concern. The authors show how the presence of an insulating layer close to the silicon etch surface makes the cryogenic etch process different from that of bulk silicon in many respects. Under these circumstances, the effects of various etch process parameters, including O2 flow, capacitively coupled rf power, substrate temperature, and chamber pressure on the etch profile quality were studied systematically on the SOI platform. The results are contrasted with bulk silicon cryogenic etching.
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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) | 0.000 | 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 | 0.000 |
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".