Comparison of the annealing characteristics of resistivity and vacancy defects for implant isolated <i>n</i>-type GaAs
Bibliographic record
Abstract
Gallium arsenide layers, Si-doped at concentrations of 2×1019, 1×1019, and 5×1018 cm−3, grown on SI substrates were implanted using multiple-energy regimes, with O+, He+, and H+, respectively, to produce resistive structures. Sample resistivity was measured following annealing in the temperature range 400–800 °C. Maximum resistivity values were achieved after annealing at 600 °C for the O+ and He+ and 500 °C for the H+ implanted sample. Equivalently implanted and annealed semi-insulating GaAs samples were analyzed using positron annihilation spectroscopy in the gamma-ray Doppler-broadening mode, a technique which is predominantly sensitive to negatively charged, or neutral, vacancy-type defects. The annealing behavior of the resistivity is in good agreement with previous reports. Vacancy defects to which the positron is sensitive are found to be removed from all semi-insulating samples at a temperature which is 100 °C below that at which maximum resistivity is achieved. Therefore, such vacancy types can be eliminated as the defect responsible for optimum electrical isolation of GaAs following implantation, and the source of vacancies necessary to annihilate such defects.
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 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".