A Hierarchical Framework for Thermal Modelling of Electronic Devices: From Atoms to Chips
Bibliographic record
Abstract
In this work, we provide an overview of a hierarchical computational framework to predict thermal transport in electronic devices through integration of physics-based models at different length scales. Information from atomistic simulations at the smallest length scales are transferred to upper levels of the hierarchy, up to thermal models for the chip. The proposed methodology includes five levels of length scales in electronic devices, namely (i) atomistic level, (ii) thin film and nanowire level, (iii) transistor and logic gate level, (iv) functional block level, and (v) chip level. At the first level of the hierarchy, properties of energy carriers in a semiconductor material (e.g., phonons) are obtained from atomistic level simulations, such as Molecular Dynamics (MD) and Lattice Dynamics (LD) calculations. At the second level, thermal transport in thin silicon films is modelled using a Lattice Boltzmann Method (LBM) for phonons. The outcome of these simulations is a size-dependent thermal conductivity for silicon films. At the third level of the hierarchy, these effective thermal conductivities are used in thermal modelling of logic gates. Detailed structures of different types of logic gates are reconstructed based on different manufacturing technologies (MOSFET and FinFET) at different technology nodes. Since the characteristic sizes of different parts of the logic gates are comparable to the mean free path of energy carriers, we use the size-dependent, effective thermal conductivities that were calculated at lower levels of the hierarchy to build simulation models for the logic gates. Based on these models, we calculate an equivalent thermal conductance for the logic gates, which would then be used in the upper level simulations to determine an equivalent thermal conductance for different functional blocks of the die based on their internal structure and the number and type of logic gates found in each functional block. Overall, the proposed hierarchical model enables us to include the effect of atomistic-level physics into package-level simulations, and thus, have an accurate prediction of thermal transport in an electronic device.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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; both teacher heads agree on what is shown here.
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".