Chromium oxide as a hard mask material better than metallic chromium
Bibliographic record
Abstract
In nanofabrication, use of thin resist is required to achieve very high resolution features. But thin resist makes pattern transferring by dry etching difficult because typical resist has poor resistance to plasma etching. One widely employed strategy is to use an intermediate hard mask layer, with the pattern first transferred into this layer, then into the substrate or sublayer. Cr is one of the most popular hard etching mask materials because of its high resistance to plasma etching. Cr etching is carried out in O2 and Cl2 or CCl4 environment to form the volatile etching product CrO2Cl2, but addition of O2 gas leads to fast resist etching. In this work, the authors show that Cr2O3 can be etched readily in a Cl2/O2 gas mixture with less oxygen than needed for Cr etching, because Cr2O3 contains oxygen by itself. Thus it is easier to transfer the resist pattern into Cr2O3 than into Cr. For the subsequent pattern transferring into the substrate here silicon using nonswitching pseudo-Bosch inductively coupled plasma-reactive ion etching with SF6/C4F8 gas and Cr or Cr2O3 as mask, it was found that the two materials have the same etching resistance and selectivity of 100:1 over silicon. Therefore, Cr2O3 is a more suitable hard mask material than Cr for pattern transferring using dry plasma etching.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".