Three-Step Room-Temperature Cleaning of Bare Silicon Surface for Radical-Reaction-Based Semiconductor Manufacturing
Bibliographic record
Abstract
In this study, a cleaning method for pregate cleaning which can suppress the generation of the microroughness of silicon-wafer surfaces as well as remove various contaminations such as metallic impurities, organic materials, and particles has been investigated. Functional waters such as ozonized ultrapure water ( -UPW), -added UPW ( -UPW), and low-concentration HF and hydrogen peroxide solution have been used in this cleaning method at room temperature. The cleaning-process flow is as follows: ( i ) -UPW cleaning, ( ii ) diluted HF and mixture including surfactant molecules (FPMS) cleaning with megasonic, and ( iii ) 30%-isopropyl alcohol/UPW rinsing, i.e., realization of three-step cleaning. After the pregate cleaning, silicon substrates are set into the radical-reaction gate-insulator film-formation process using microwave-excited, high-density plasma equipment, where or ion bombardment onto the substrate surface has been introduced to eliminate absorbed surfactant molecules as well as hydrogen termination, before starting oxygen radical oxidation or radical nitridation . This advanced, three-step room-temperature cleaning is promising for future semiconductor manufacturing in conjunction with the newly developed radical-reaction-based semiconductor manufacturing.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".