SiGe-on-insulator fabricated via germanium condensation following high-fluence Ge+ ion implantation
Bibliographic record
Abstract
Germanium condensation is demonstrated using a two-step wet oxidation of germanium implanted Silicon-On-Insulator (SOI). Samples of 220 nm thick SOI are implanted with a nominal fluence of 5 × 1016 cm−2 Ge+ at an energy of 33 keV. Primary post-implantation wet oxidation is performed initially at 870 °C for 70 min, with the aim of capping the sample without causing significant dose loss via Ge evaporation through the sample surface. This is followed by a secondary higher temperature wet oxidation at either 900 °C, 1000 °C, or 1080 °C. The germanium retained dose and concentration profile, and the oxide thickness is examined after primary oxidation, and various secondary oxidation times, using Rutherford backscattering analysis. A mixed SiGe oxide is observed to form during the primary oxidation followed by a pure silicon oxide after higher temperature secondary oxidation. The peak germanium concentration, which varies with secondary oxidation condition, is found to range from 43 at. % to 95 at. %, while the FWHM of the Ge profile varies from 13 to 5 nm, respectively. It is also observed that both the diffusion of germanium and the rate of oxidation are enhanced at 870 and 900 °C compared to equilibrium expectations. Transmission electron microscopy of a representative sample with secondary oxidation at 1080 °C for 20 min shows that the SiGe layer is crystalline in nature and seeded from the underlying silicon. Raman spectroscopy is used to determine residual strain in the SiGe region following secondary oxidation. The strain is compressive in nature and increases with Ge concentration to a maximum of approximately 1% in the samples probed. In order to elucidate the physical mechanisms, which govern the implantation-condensation process, we fit the experimental profiles of the samples with a model that uses a modified segregation boundary condition; a modified linear rate constant for the oxidation; and an enhanced diffusion coefficient of germanium where the enhancement is inversely proportional to the temperature and decays with increasing time. Comparison of the modeled and experimental results shows reasonable agreement and allows conclusions to be made regarding the dominant physical mechanisms, despite the semi-empirical nature of the model used.
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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".