Photonic integration of InGaAs/InGaAsP laser using low energy arsenic implantation induced disordering for quantum well intermixing
Bibliographic record
Abstract
Quantum well intermixing (QWI) using a neutral impurity induced disordering technique is of great interest in producing photonic integrated circuits (PICs). We report a high selectivity QWI process using a low energy arsenic implantation induced disordering technique. Since it is known that free electrons from impurities result in high optical absorption and degrade the quality of the material after intermixing, arsenic, an electrically neutral species in the InGaAs/InGaAsP system, was chosen for the process development. The relatively low implantation energy, 360 keV, reduces the damage generation and results in a shallow implantation depth far away from the active region. We have successfully blue shifted quantum well laser material with a control on the amount of intermixing by varying the dose of As implantation at 200/spl deg/C. A wide range of differential bandgap shifts going up to 60 meV are reported. PICs such as extended cavity lasers and monolithic multiple wavelength laser sources are currently being investigated using this technique.
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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".