Profile evolution simulator for sputtering and ion-enhanced chemical etching
Bibliographic record
Abstract
A plasma etching profile simulator was developed to investigate the evolution of pattern profiles in various materials under different plasma conditions. This simulator is based on a two-dimensional cellular method. The model is fed with input parameters that include angular dependent etch yield, ion and neutral angular distribution, and plasma and material characteristics. It has been tested by comparison with published profiles of Si sputtering and SiO2 ion-assisted chemical etching in argon and chlorine plasmas. Observed microtrenching and bowing have been well reproduced by the simulator. The simulator was further used to examine etching for dimensions below nanometer in low-pressure high-density plasmas. In the case of Si sputtering, trenches of 100 nm depth and 30 nm or less width show unusual lateral etching. Finally, the effect of positive charge accumulation on an insulated mask resulting from negative bias voltage on the wafer was studied. This charge accumulation causes a deflection of ion trajectories. Considering this phenomenon, very isotropic etched profiles were found, in good agreement with in-house experimental profiles of platinum sputtering in argon plasma. The simulator developed is intended to be used for any material and mask combination in order to predict the profile evolution under various plasma conditions and pattern dimensions from micrometer to nanometer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".