Optimal susceptor rotation speed in hot‐wall horizontal <scp>SiC</scp> epitaxy using computational fluid dynamics
Bibliographic record
Abstract
Abstract Silicon carbide (SiC) semiconductors are critical for high‐temperature, high‐power, and high‐frequency electronic devices due to their high thermal conductivity and wide bandgap. Uniform SiC epitaxial layers in chemical vapour deposition (CVD) reactors are essential for consistent electrical properties and enhanced wafer productivity. This study investigates hydrodynamics and concentration distribution in a custom‐designed hot‐wall horizontal SiC‐CVD industrial‐scale reactor using a Eulerian computational fluid dynamics (CFD) model to optimize susceptor rotation speed for uniform 8‐inch SiC epitaxial thickness. The inlet gas mixture (H 2 , N 2 , C 2 H 4 , SiHCl 3 ) enters at 700°C and is preheated to 1200°C. An 8‐inch wafer is positioned on a susceptor rotating at 0–300 rpm and heated to 1700°C. Grid convergence index analysis verified mesh independence. The realizable k‐ε turbulence model provided the highest accuracy among four turbulence models. CFD results for SiHCl 3 concentration closely matched experimental SiC film thickness profile. The Rossby number (Ro), representing the ratio of coriolis to inertial effects, explains swirling flow formation, which reduces the SiHCl 3 concentration uniformity index () at high rotation speeds (250 and 300 rpm). CFD results reveal an optimal rotation speed of 200 rpm for maximum . This study provides a robust CFD‐based framework for optimizing SiC‐CVD reactor parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".