Selective area bandgap engineering of InGaAsP/InP quantum well microstructures with an infrared laser rapid thermal annealing technique
Bibliographic record
Abstract
Fabrication of wafers with built-in areas of different bandgap materials is of paramount importance for the technology of monolithically integrated devices. Numerous approaches have been proposed and investigated in literature to address this problem especially in III-V basedsemiconductor microstructures. We report on an innovative technique of post-growth selective area bandgap engineering of InGaAsP/InP quantum well (QW) microstructures that is based on infrared laser rapid thermal annealing (Laser-RTA). The method makes use of a 150 W 980 nm laser for background heating of wafers to just below the threshold for quantum well intermixing (QWI) temperatures. Another infrared source, a 30 W TEM00 Nd:YAG laser, is used to increase the temperature above the QWI threshold that leads to the fabrication of different bandgap material. The Laser-RTA technique allows for a significant reduction in the risk of damaging the surface of a semiconductor wafer heated to high temperature with one laser source. Also, it has the potential to fabricate almost arbitrary shaped lines of bandgap engineered material. For the investigated GaInAsP/InP QW microstructures, we have achieved bandgap shifts in excess of 200 nm. We discuss advantages that the proposed Laser-RTA technique offers in the fabrication of monolithically integrated photonic devices.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".