Transient thermal characterization with applications to optimized thermal packaging of multi-core microprocessors
Bibliographic record
Abstract
As the increase of power densities became the primary constraint for semiconductor industry to sustain the Moore’s law for microprocessor evolution, multi-core architecture has been introduced in order to meet the growing demands for performance. Non-uniform power distribution, increased diesize and multiple-chip packaging present new challenges for the thermal management of modern CPUs. Further development of packaging technology and advanced thermal interface materials (TIMs) requires both maximization of the total thermal throughput of the system and mitigation of the thermal impact from non-uniformly distributed hotspots introduced by individual cores. Therefore, thermal characterization techniques capable of resolving thermal resistance distribution at TIM1 level need increased emphasis in package development. This work aims to develop practical techniques for such characterization. Steady-state measurements are supplemented by transient techniques to allow thorough characterization of thermal performance of CPU packages. Such techniques would aid with development of optimized thermal packaging to meet new challenging thermal requirements imposed by modern computing architectures.
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 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 | 0.001 | 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 | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".