The Role of Oxygen on Anisotropy in Chromium Oxide Hard Mask Etching for Sub-Micron Fabrication
Bibliographic record
Abstract
Chromium and its oxides have been playing a vital role in the fabrication of micro- and nano-scale structures in numerous applications for several decades. Controllable, robust and anisotropically dry-etched hard masks and their optimal etch recipes are required in state-of-the-art device fabrication techniques. In terms of manufacturability and repeatability, a mechanistic understanding of the plasma-etching process of chromium oxide (Cr2O3) is necessary for its adoption as a hard mask. We present a systematic investigation of plasma etching of chromium oxide films via an inductively coupled plasma-reactive ion etching (ICP-RIE) system in nanoscale. The effects of plasma composition, ICP source power and HF platen power on the etch rate, sidewall profile, surface morphology, and dc-bias have been methodically investigated. We paid particular attention to studying how oxygen content can be used to control the etch profile of nano trenches using chlorine/oxygen gas mixtures, including extremes of very low and very high oxygen content. It was found that chromium oxide etch mechanisms are dependent strongly on the oxygen level. We achieved desirable vertical sidewalls with reasonable etch rates when the oxygen content is in the range 10-40% in the plasma. Oxygen content below 10% resulted in positively tapered etch profiles with low etch rates. On the other hand, bowl-like etch profiles with undercut formation was observed at high oxygen content above 40%, caused by re-emission of the reactive species at this regime. As a hard mask material, patterning Cr2O3films compared to Cr metal is advantageous in terms of etch uniformity and reproducibility. Contrary to Cr, Cr2O3is not as sensitive to chamber wall conditions.
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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".