Two-Phase Immersion Cooling of Microprocessors with Electroplated Porous Heat Spreaders: Thermal Performance and Reliability
Bibliographic record
Abstract
We conducted a thermal performance and reliability study of a new two-phase cooling prototype that localizes the cooling over a CPU, after enhancing the top side of its exposed integrated heat spreader with a 500-micron thick Multi-Scale Electroplated Porous coating. The cooling prototype used 280 mL of Novec™ 7000 dielectric fluid and 237 m3/h of airflow. Junction-to-air thermal resistance measurements were performed with the boiling occurring directly over a standard nickel-plated integrated heat spreader, as well as on the modified heat spreader. When compared to boiling over the unmodified CPU, boiling over the electroplated CPU lowered the junction-to-air thermal resistance by 56% at the lowest power dissipation and by 51% at the highest power dissipation (109.5±1.7) W. The lowest junction-to-air thermal resistance of (0.225±0.003)°C/W was achieved over the modified heat spreader at (109.5±1.7) W, compared to (0.436±0.006)°C/W for the unmodified CPU. The cores temperature was also reduced by 18.1°C and 26.4°C at (41.6±0.2) W and (109.5±1.7) W power, respectively. The highest cores temperature for the CPU with modified heat spreader was only 45.4°C at (109.5±1.7) W. This demonstrates the performance improvement potential when optimizing the standard integrated heat spreaders of processors for cooling by immersion in dielectric fluids. No significant differences were observed for the inside pressure of the systems between the two boiling surfaces, suggesting that the nature of the boiling surface does not affect the build-up pressure inside such closed cooling systems. Cooling prototypes with the modified heat spreaders underwent reliability tests, with their CPU being repeatedly stressed to maximum power for 15 minutes and then put in an idle state for the same duration, a total of 2000 times (1000 hours). No change to the pressure or degradation in the thermal performance was recorded, suggesting that the sealing mechanism is reliable and prevents air infiltration over extended periods of use. The electroplated surface presented no visible damage to its structure, thus suggesting that the proposed localized two-phase cooling technology can achieve both high thermal performance and reliable operation.
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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) | 0.000 | 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) | 0.001 | 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".