Thermal Simulator for Advanced Packaging and Chiplet-Based Systems
Bibliographic record
Abstract
Heterogeneous chiplet-based integration is expected to provide performance scalability and cost-effectiveness for the next generation of microelectronic systems. Practical deployment of chiplet-based platforms, however, requires developing novel electronic design automation (EDA) tools that support advanced packaging approaches. Compact thermal simulators are essential EDA tools for the evaluation of design alternatives at the early stages of the design. Developing efficient compact thermal simulators for advanced heterogeneous integration platforms is a key requirement, as the available tools provide limited support for heterogeneity and advanced packaging technologies. ARTSim 2.0, a robust thermal simulator for heterogeneous integration platforms, is presented in this work. ARTSim 2.0 includes three main features, i.e., robust hybrid meshing, modeling of heterogeneous layers, and an efficient solver that utilizes parallel processing. Several case studies on advanced chiplet-based platforms, including TSV-based 3-D integrated circuits (ICs), Intel EMIB, and TSMC InFO_PoP, are conducted to demonstrate the novel capabilities of ARTSim 2.0. The performance of ARTSim 2.0 for both transient and steady-state conditions is compared to results obtained from state-of-the-art finite element method (FEM) tools. Simulation results confirm that the temperature accuracy of the thermal maps that are generated by ARTSim 2.0 is within a maximum error of 1.17% while exhibiting a reduction in runtime of at least two orders of magnitude, as compared to the FEM tools.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".