Thermal Analysis of System in Package Considering Boundary Conditions for Long-Term Reliability Studies
Bibliographic record
Abstract
This paper proposes an integrated Foster-based thermal network for a System in Package (SiP) that models thermal interaction between package layers to predict the transient temperature of junctions and interlayer compression in SiP. The critical factors contributing to impedance mismatch are detailed to ensure modeling accuracy. Important factors include the boundary conditions and the interface layer of the thermal material. With the help of the Finite Element Method (FEM) and data curve fitting, thermal parameters are derived and expressed as a function of boundary conditions. The proposed modeling method is demonstrated with 3D heterogeneous System in Package models implemented in Simulink for long-term temperature predictions. Predicted junction and interlayer temperatures show good accuracy, confirmed by reported results obtained by Finite Element Analysis (FEA). The importance of considering the boundary conditions and the materials used in the various interfaces is shown through simulation results. Neglecting one of these key factors, the predicted temperature differs by as much as 14.5 °C in the reported results. The proposed thermal network is consistent with FEA, while computing much faster and producing results that differ by no more than 0.5 °C, unlike previously reported models.
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How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".