Study of Ultra-Thin Silicon Micropillar Based Vapor Chamber
Bibliographic record
Abstract
Silicon vapor chamber has drawn much attention in recent years, owing to its mature microfabrication process, accurate control over its geometries, direct integration with semiconductor devices that can eliminate the thermal interface resistance and prevent thermal expansion mismatch. In this paper, micropillar wicks that can generate excellent capillary performance and possess high permeability was adopted as the evaporator and adiabatic wick structure. Ultra-thin vapor chambers with thickness of only 1.25 mm were fabricated, the total size of the vapor chamber was 4 cm4 cm1.25 mm. As essential parts of a vapor chamber, the geometric sizes of the micropillars at evaporator and adiabatic regions were optimized. Based on the Brinkman equation derived dryout heat flux model, the optimal geometric combinations for evaporator and adiabatic regions were d =17.4 m, h = l =30.6 m and d =15 m, h = l =30.6 m respectively. Actual sizes after micro fabrication was d =18.9 m, h =31.3 m, l =30.6 m and d =15.8 m, h =31.3 m, l =30.6 m for evaporator and adiabatic regions respectively. Performance comparison between optimized and nonoptimized samples has shown that the optimized sample performed best, which can dissipate a high heat flux of 98.1 W/cm 2 before dryout. The deviation between model predicted and experimentally measured dryout heat flux was only 11.2 %, which validated the model with high accuracy. Effective thermal resistance of various samples was also studied. A smallest effective thermal resistance of 0.53 K/W can be obtained. Effective thermal resistance was found to decrease with heat flux before dryout while a reverse trend was observed after dryout. The vapor chamber was also found to have a good temperature uniformity. The largest temperature difference was only 9.6 C at very high heat load of 98.1 W. This paper demonstrated significant insights into the investigation of silicon vapor chamber, and can be used as useful design guidance for micropillar based vapor chambers.
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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 | 0.000 |
| 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 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".