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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".