Role of the electron–phonon coupling in tuning the thermal boundary conductance at metal-dielectric interfaces by inserting ultrathin metal interlayers
Bibliographic record
Abstract
Varying the thermal boundary conductance at metal-dielectric interfaces is of great importance for highly integrated electronic structures such as electronic, thermoelectric and plasmonic devices where heat dissipation is dominated by interfacial effects. In this paper we study the modification of the thermal boundary conductance at metal-dielectric interfaces by inserting metal interlayers of varying thickness below 10 nm. We show that the insertion of a tantalum interlayer at the Al/Si and Al/sapphire interfaces strongly hinders the phonon transmission across these boundaries, with a sharp transition and plateau within ∼1 nm. We show that the electron-phonon coupling has a major influence on the sharpness of the transition as the interlayer thickness is varied, and if the coupling is strong, the variation in thermal boundary conductance typically saturates within 2 nm. In contrast, the addition of a nickel interlayer at the Al/Si and the Al/sapphire interfaces produces a local minimum as the interlayer thickness increases, due to the similar phonon dispersion in Ni and Al. The weaker electron-phonon coupling in Ni causes the boundary conductance to saturate more slowly. Thermal property measurements were performed using time domain thermo-reflectance and are in good agreement with a formulation of the diffuse mismatch model based on real phonon dispersions that accounts for inelastic phonon scattering and phonon confinement within the interlayer. The analysis of the different assumptions included in the model reveals when inelastic processes should be considered. A hybrid model that introduces inelastic scattering only when the materials are more acoustically matched is found to better predict the thickness dependence of the thermal boundary conductance without any fitting parameters.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".