Thermo-mechanical co-design of 2.5D flip-chip packages with silicon and glass interposers via finite element analysis and machine learning
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".