Cryogenic Single-Component Micro-Nano Solid Nitrogen Particle Production Using Laval Nozzle for Physical Resist Removal-Cleaning Process
Bibliographic record
Abstract
The innovative characteristics of the cryogenic single-component micro-nano solid nitrogen (SN2) particle production using super adiabatic Laval nozzle and its application to the physical resist removal-cleaning process are investigated by a new type of integrated measurement coupled computational technique. The originality to be noted in the present study is that the continuous production of micro-nano SN2 particle is achieved by using single component gas-liquid two-phase flow of subcooled nitrogen through a Laval nozzle (converging-diverging nozzle). As a result of present computation, it is found that high-speed ultra-fine SN2 particle is continuously generated due to the freezing of liquid nitrogen (LN2) droplet induced by rapid adiabatic expansion of subsonic subcooled two-phase subcooled nitrogen flow passing through the Laval nozzle. Furthermore, the effect of SN2 particle diameter, injection velocity, and attack angle to the wafer substrate on resist removal-cleaning performance is investigated in detail by integrated measurement coupled computational technique.
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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) | 0.000 | 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".