Ultra-highly doped Si1−xGex(001):B gas-source molecular-beam epitaxy: Boron surface segregation and its effect on film growth kinetics
Bibliographic record
Abstract
Si 1−x Ge x (001) layers doped with B concentrations CB between 2×1016 and 2×1021 cm−3 were grown on Si(001)2×1 at Ts=500–700 °C by gas-source molecular-beam epitaxy (GS-MBE) from Si2H6, Ge2H6, and B2H6. Secondary-ion mass spectrometry measurements of modulation-doped structures demonstrate that B doping has no effect on the Ge incorporation probability. Steady-state B and Ge surface coverages (θB and θGe) were determined as a function of CB using in situ isotopically tagged temperature-programmed desorption. Results for Si0.82Ge0.18 layers grown at Ts=500 °C show that θGe remains constant at 0.63 ML while the bulk B concentration increases linearly up to 4.6×1020 cm−3, corresponding to saturation coverage at θB,sat=0.5 ML, with the incident precursor flux ratio ξ=JB2H6/(JSi2H6+JGe2H6). B is incorporated into substitutional electrically active sites over this entire concentration range. At higher B concentrations, CB increases faster than ξ and there is a large decrease in the activated fraction of incorporated B. The B segregation enthalpy during Si0.82Ge0.18(001) growth is −0.42 eV, compared to −0.53 and −0.64 eV during Si(001):B and Ge(001):B GS-MBE, respectively. Measured segregation ratios rB=θB/xB, where xB is the bulk B fraction, range from 15 to 500 with a temperature dependence which is consistent with equilibrium segregation. Film deposition rates RSiGe(CB) decrease by up to a factor of 2 with increasing CB⩾5×1019 cm−3, due primarily to a B-segregation-induced decrease in the dangling bond density. The above results were used to develop a robust model for predicting the steady-state H coverage θH, θB, θGe, and RSiGe as a function of ξ and Ts.
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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 | 0.000 |
| 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) | 0.001 | 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".