Reliability analysis based on cascaded-Foster thermal networks for systems-in-package (SiP)
Bibliographic record
Abstract
This paper presents a fast and comprehensive method for reliability prediction of 3D System-in-Package (3D SiP) technologies. The proposed approach accounts for both critical wear-out failure mechanisms and mission-specific profiles. A novel reliability assessment framework is introduced to address the limitations of traditional methods, which often overlook the variability in failure mechanisms and wear-out rates across different layers within the same mission profile. A key contribution of this work is the coupling between the mission profile and dominant wear-out rates, enabling simultaneous consideration of multiple failure mechanisms, such as those affecting through-silicon vias (TSVs) and solder joints, under real-time operating conditions. The framework offers a multilayer analysis that uses unified units for each layer and incorporates a precise thermal model, enabling the rapid prediction of thermal behavior throughout the system's lifetime. Additionally, the proposed method can be easily integrated into circuit simulators and utilized as a real-time reliability estimator. This capability enables researchers and engineers to further investigate interactions between failure mechanisms across different layers of the SiP under realistic and dynamic operational conditions. By identifying the dominant failure mechanism in each layer, the method supports early-stage design decisions to mitigate potential reliability issues. The reliability estimation process involves selecting the mechanism with the shortest predicted lifespan for each layer and constructing the overall reliability curve using a series configuration of the reliability block diagram. Long-term mission profiles are translated into thermal loads through a cascaded Foster thermal network, with Monte Carlo simulations applied to determine the system's failure distribution.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".