Full-Wave Electromagnetic Simulation Approach for Integrated 3D-IC Design
Bibliographic record
Abstract
This paper utilizes FEM (Finite Element Method) based full-wave 3D electromagnetic simulation tool by Ansys HFSS (High-Frequency Structure Simulator) to solve complex 3D-IC systems. Fusion of powerful advanced meshing and solving technologies referred to as “Mesh Fusion”, is employed to handle different scales of geometries or regions ranging from nanometers to centimeters. Each region including Package, Interposer, logic die (CPU, Central Processing Unit), and eight stacked HBM dies are meshed parallelly and independently by optimum mesh algorithms. Fully coupled fields are generated across different regions. CPU Die and HBM's are connected through a silicon interposer placed on a Ball-Grid Array package and are communicating at a Nyquist data rate of 5.8 Gbps. Signal integrity parameters such as return loss, insertion loss, near and far end crosstalk parameters, and eye diagram opening are observed for high-speed data, strobe lines between the CPU die and HBM. The significance of performing integrated 3D-IC simulation in comparison to isolated standalone interposers on these parameters is also investigated. In addition, electric field coupling among different regions is studied in detail.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".