Evolution of thermal, structural, and optical properties of SiGe superlattices upon thermal treatment
Bibliographic record
Abstract
We investigate the evolution of the cross‐plane thermal conductivity κ of a SiGe superlattice (SL) as it is gradually converted into an alloy via post‐growth thermal treatment at temperatures varying from 650 to 1000 °C. X‐ray diffraction (XRD), transmission electron microscopy (TEM), secondary‐ion‐mass‐spectroscopy (SIMS), and photoluminescence (PL) spectroscopy measurements are used as complementary tools to gain insight into the structural properties of our SL and their evolution upon annealing. While the SL structure is preserved up to temperatures of ∼850 °C, full alloying is observed for higher temperatures. The thermal conductivity data, collected with differential 3 ω method, show a monotonic increase of κ from ∼4.5 W/m K up to the values expected for a thin‐film alloy. To understand the results, we compute the phonon mean‐free‐path (MFP) spectra using the experimentally determined composition profiles as input. The calculated thermal conductivity values are in good agreement with the experimental data and show that the increase of thermal conductivity is due to a gradual increase of MFP of low‐to‐mid‐frequency phonons, i.e., to a weakening of interface scattering for alloyed SLs. The calculations also allow us to address finite‐thickness effects on the measured thermal conductivity data. Although the used SL had a total thickness of only ∼250 nm, its thermal conductivity can be assumed to coincide with that of a “bulk” (infinitely thick) SL prior to annealing. As interdiffusion increases, boundary scattering at the film/substrate interface becomes relevant and leads to a slow increase of κ . Besides its fundamental relevance, this work shows that the thermal stability of superlattices is limited, and that sizeable structural changes (including defect formation) occur already at operation temperatures of ∼650 °C.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".