Thin micro-cold plate for hot-spot aware chip cooling
Bibliographic record
Abstract
This work proposes a non-invasive and hot spot aware cooling approach by stacking a micro-cold plate at the backside of a chip. With trends such as 3D stacking and hotspot generation, microelectronics face major cooling challenges to ensure chip performance and reliability. It makes the liquid microchannel solutions more adapted than conventional air cooling for both space and heat removal. One approach is to concentrate the cooling in the vicinity of the heat sources to help to judiciously use the pumping power and contribute to keep the cooling solution thin and easily integrated. An experimental micro-cold plate has been fabricated through wafer level produced microchannels, capped with die-to-wafer pick-and-place operation. The microchannels from the cooling die are formed by Si DRIE and the die is capped with a Si wafer attached by SiNR adhesive on one side. An epoxy adhesive is then bonded to a thermal test chip with metallic lines as heaters and temperature sensors for a total stack thickness of 1.5 mm. It has then been characterized on a dedicated test bench. A cooling resistance of 3.5 °C/W is achieved with an electric power of only 1.2 W, showing a coefficient of performance of 5770 in respect of an hydraulic power of 2.6 mW and with a 609 W/cm2heat flux. Finally, such micro-cold plate could be used as an “add-on solution” in applications where space and pumping power are limited, independently of the chip thickness or the possibility of etching its back surface.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".