Thermo-mechanical co-design of 2.5D flip-chip packages with silicon and glass interposers via finite element analysis and machine learning
Why this work is in the frame
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Bibliographic record
Abstract
Advanced 2.5D flip-chip packages with silicon/glass interposers may pose tightly coupled thermo-mechanical trade-offs. This work presents a simulation-driven, machine-learning-assisted co-design framework that links high-fidelity finite-element analysis (FEA) with surrogate modeling, multi-objective optimization, and decision analysis. A 3D FEA model generates 500 Latin Hypercube design points for type of analysis (thermal and reliability), spanning geometry, materials, and thermal-path variables. Four minimized objectives are considered: junction-to-ambient thermal resistance ( Θ JA ) and cycle-averaged plastic strain-energy density at the corner flip-chip cu-pillar bump ( Δ W bump ), C4 bump ( Δ W C 4 ), and BGA ( Δ W BGA ). Tree-based regressors (Random Forest, XGBoost) achieve high test-set fidelity and drive NSGA-II to enumerate the Pareto domain. A Net Flow multi-criteria decision method (MCDM) ranks Pareto candidates to identify a champion design with balanced thermo-mechanical performance. Re -simulation of the champion in FEA confirms surrogate accuracy for dominant responses (≈4–5 % deviation for Δ W bump and Δ W C 4 ) and exact agreement for Θ JA , while revealing weak coupling between thermal and mechanical objectives—enabling partial decoupling of heat-path optimization from interconnect reliability.
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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 | 0.000 |
| 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 it