Novel Peak-Source-Scanning (NPSS) Model for Thermal Control of Systems-in-Package (SiP)
Bibliographic record
Abstract
One of the fast-growing electronic integration technologies in the modern high-density microelectronics industry is System-in-Package (SiP). It is expected to accelerate application development when reducing implementation risks with optimized codes. However, monitoring the thermal behavior of every chip in SiPs is challenging. This paper proposes a Novel Peak Source-Scanning (NPSS) algorithm based on the Gradient Direction Sensors (GDS) method. The proposed algorithm can detect and locate thermal peaks on any SiP. Detecting such peaks is vital for thermal monitoring and stress management on high-density semiconductor devices to avoid induced thermo-mechanical stresses. Furthermore, the NPSS algorithm can manage and monitor silicon chips with Multiple Heat Sources (MHS). To assess this algorithm, we used tools from COMSOL Multiphysics® and MATLAB® for Temperature-prediction (Tp), and Temperature-estimation (Te), respectively. Our simulations use the generalized GDS methodology for MHS using the finite element method (FEM) to highlight our NPSS capabilities to predict on-chip thermal peaks with a maximum error of 1.27 K (Kelvin).
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".