Evaluation of Embeddable FANTASTIC BCI-ROMs as Compact Thermal Models in Electronic Cooling applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 standardized <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1, 2</sup> and 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.
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.
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 | 0.000 | 0.000 |
| 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 it