Evaluation of Embeddable FANTASTIC BCI-ROMs as Compact Thermal Models in Electronic Cooling applications
Bibliographic record
Abstract
System integrators rely on semiconductor vendors to provide thermal models of their products so that in-situ thermal performance can be simulated during the design process. Currently, this provision of models is impeded in several ways. Calibrated detailed thermal models, while representing the pinnacle of accuracy, are seldom available due to intellectual property protection concerns, and when they are, the level of detail included is computationally expensive to include in 3D Computational Fluid Dynamics (CFD) simulations. Compact thermal models are standardized1, 2and widely available, however these are mostly obsolete as they do not support transient applications and they do not support multiple die packages. System integrators must then resort to creating approximate detailed models which is time consuming and adds unquantified uncertainty to the results. This paper will introduce a method to solve these problems and enable the thermal model supply chain for packages by embedding a boundary condition independent reduced order model (BCI-ROM) in a 3D CFD thermal simulation tool.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".