Reduced Order Modeling of Transient Heat Transfer in Microchip Interconnects
Bibliographic record
Abstract
The high current densities in today's microelectronic devices and microchips lead to hotspot formations and other adverse effects on their performance. Therefore, a computational tool is needed to not only analyze but also accurately predict spatial and temporal temperature distribution while minimizing the computational effort within the chip architecture. In this study, a proper orthogonal decomposition (POD)-Galerkin projection-based reduced order model (ROM) was developed for modeling transient heat transfer in three-dimensional (3D) microchip interconnects. comsol software was used for producing the required data for ROM and for verifying the results. The developed technique has the ability to provide accurate results for various boundary conditions on the chip and interconnects domain and is capable of providing accurate results for nonlinear conditions, where thermal conductivity is temperature dependent. It is demonstrated in this work that a limited number of observations are sufficient for mapping out the entire evolution of temperature field within the domain for transient boundary. Furthermore, the accuracy of the results obtained from the developed ROM and the stability of accuracy over time is investigated. Finally, it is shown that the developed technique provides a 60-fold reduction in simulation time compared to finite element techniques.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".