Enhancing Thermal Security of 3D-SiP Systems through Thermal Digital Twin (TDT)
Bibliographic record
Abstract
Thermal attacks targeting large-scale integrated microsystems, such as three-dimensional package systems (3D-SiP), exploit thermal fluctuations to compromise their security. These attacks can lead to excessive thermal dissipation, causing hardware failures or enabling the extraction of sensitive information such as cryptographic keys by analyzing heating and cooling patterns. Therefore, the design of integrated microsystems must integrate thermal management and security to prevent vulnerabilities to such attacks. In this article, we propose an innovative approach to improve the prediction of system-in-package (SiP) thermal dynamics by developing a thermal digital twin (TDT) using COMSOL Multiphysics software. This method accurately replicates the thermal behaviors of an existing physical system, specifically the Xilinx SPARTAN-3E field-programmable gate array (FPGA) board, using thermal data collected by the Gradient Direction Sensor (GDS) technology integrated into the Xilinx FPGA. Our TDT offers a cutting-edge solution for monitoring and examining thermal fluxes in 3D-SiPs, representing a significant advancement in the thermal regulation of these microsystems and implicitly enhancing their thermal security.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".