Impact of integrating microchannel cooling within 3D microelectronic packages for portable applications
Bibliographic record
Abstract
This work presents the impact of integrating microfluidic channels at different locations in a 3D stacked chip configuration to improve its thermal management. Bringing fluidic cooling within the microelectronic package can allow the use of higher power chips in limited space applications, but suitable sites for the microchannels in packaged 3D stacks must be determined. The approach uses an analytical representation of microfluidics and heat transfer at the package level to evaluate the equivalent thermal resistances. This analytical lumped-element circuit model is used to compare different microchannel cooling configurations, for 3D stacked chips. With the addition of microfluidic cooling, the allowable power level increases dramatically. The study also shows that locating the fluid cooling inside or adjacent to the chip stack reduces the thermal resistance over 6 times compared to microchannels located at the surface of the molding or the ball grid array. Within the chip stack however, the location does not have a noticeable impact. The actual thermal design power of microprocessors for portable applications has the potential to be doubled by using microfluidic cooling with moderate flow rates and pressure drops.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".