Vacuum-Assisted Wet Processing for Advanced 3D Devices
Bibliographic record
Abstract
ABSTRACT The penetration of liquid into high aspect ratio (HAR) features presents a challenging situation in device fabrication where the removal of residue and/or oxide inside the structures is critical before processing through the subsequent steps. Conventional methods have proven inadequate for getting chemical to reach the bottom of such structures. This paper examines the use of a vacuum priming wet immersion method to introduce liquid chemicals or rinse water throughout the entire HAR feature prior to the oxide etching step. The data show that by pulling a vacuum below the saturated vapor pressure of water, liquid can be successfully drawn into the entire feature, enabling a uniform oxide etch. Conversely, without the initial vacuum priming step the etching will not be uniform and oxide will remain at the bottom of the trench or via. The results confirm the physical dynamics within such features as well as the need for a more in-depth study to fully understand the vacuum priming and drying mechanisms as they relate to different aspect ratios and geometries.
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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".