Development of a flat heat pipe embedded in a near-millimeter thick ceramic electronics substrate
Bibliographic record
Abstract
Thin ceramic substrates are widely used in electronics for their high dielectric strength ( ∼ 12–16 kV mm −1 ), low dielectric constant ( ∼ 8–13 at 1 MHz–1 GHz), low coefficient-of-thermal expansion ( ∼ 4–8 ppm °C −1 ), compactness ( ∼ 0.1–1 mm), moderate thermal conductivity ( ∼ 20–180 W m −1 K −1 ), and superior corrosion resistance. Although ceramics possess thermal conductivities that surpass those of organic substrates ( ∼ 0.1–0.5 W m −1 K −1 ), they remain prohibitively lower than conventional heat-spreading materials like copper ( ∼ 400 W m −1 K −1 ). This significantly limits their role in package-level thermal management. For the first time, the present study demonstrates a thin ceramic substrate-embedded flat heat pipe that can substantially overcome these limitations. The thin ceramic flat heat pipe is 51 mm in width and 127 mm in length, with a slim overall thickness of 1.3 mm, which is less than half the thickness of the thinnest ceramic heat pipe reported in the literature. It is fabricated monolithically by tape casting with laser-machined, below 100 μm-width connected wicking microstructures. It weighs only 20 g, 72% lighter than a solid copper sheet. It significantly outperforms a solid copper sheet above marginal heat transfer rates, achieving effective thermal conductivities of up to 566, 2182, and 8392 W m −1 K −1 in the antigravity, horizontal, and gravity-assisted orientations, respectively, which is highly competitive with those of much thicker ceramic heat pipes in the literature.
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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 | 0.001 | 0.000 |
| 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".