Interconnect Joule Heating under Transient Currents using the Transmission Line Matrix Method
Bibliographic record
Abstract
The quality and reliability of interconnects in microelectronics is a major challenge considering the increasing level of integration and high current densities. This work studied the problem of transient Joule heating in interconnects in a two-dimensional (2D) inhomogeneous domain using the transmission line matrix (TLM) method. Computational efficiency of the TLM method and its ability to accept non-uniform 2D and 3D mesh and variable time step makes it a good candidate for multi-scale analysis of Joule heating in on-chip interconnects. The TLM method was implemented with link-resistor (LR) and link-line (LL) formulations, and the results were compared with a finite element (FE) model. The overall behavior of the TLM models were in good agreement with the FE model while, near the heat source, the transient TLM solutions developed slower than the FE solution. The steady-state results of the TLM and FE models were identical. The two TLM formulations yielded slightly different transient results, with the LL result growing slower, particularly at the source boundary and becoming unstable at short time-steps. It was concluded that the LR formulation is more accurate for transient thermal analysis.
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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) | 0.000 | 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 | 0.001 |
| 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 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".