The creation of compact thermal models of electronic components using model reduction
Bibliographic record
Abstract
This paper presents a new approach to create boundary condition independent thermal compact models based on the multidimensional model reduction (MDMR) technique. A methodology is developed for the generation of a multi dimensional compact model (MDCM) from a detailed numerical model. The MDCM is shown to have a number of advantages over resistor network models. The generation of the model is at least an order of magnitude faster then the creation of an optimized network model. The MDCM displays very high accuracy typically better than 0.1%, is very flexible allowing for the prediction of all internal temperatures, and presents no limitations on the external configuration of the compact model. A generic multi-chip module ball grid array (MCMBGA) package is used to demonstrate the technique. The MDCM created shows to have high predictive capability, boundary condition independence and a small model size. Finally, by connecting the MDCM to a printed circuit board model and simulating the system, speed ups of around 100 times are achieved.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".