Surface morphology of SiO2 coated InP/InGaAs/InGaAsP microstructures following irradiation with the ArF and KrF excimer lasers
Bibliographic record
Abstract
Successful fabrication of devices from quantum well-intermixed material requires efficient control of its surface morphology. To address this problem, we have employed atomic force microscopy to study surface morphology of InP/InGaAs/InGaAsP QW microstructure coated with dSiO2 = 50, 150, 190, 243 and 263 nm thick SiO2 films. Both ArF (193 nm) and KrF (248 nm) excimer lasers have been used to irradiate series of samples with up to 400 pulses of fluence 76 to 156 mJ/cm2. The roughness (σRMS) of SiO2 layer after both lasers irradiation and RTA decreases as the pulse number increases. Following RTA, a smoother surface morphology was observed for all irradiated samples. The cap InP layer was found to have a relatively smaller roughness (~ 0.4 nm) due to the protection provided by the SiO2 layer during excimer laser irradiation and high temperature RTA. For samples coated with 50- or 150-nm-thick SiO2 and irradiated by the ArF laser, the blueshift is only obtained when the SiO2 layer was ablated. However, the sample coated with 243-nmthick SiO2 (dSiO2 ≈ λKrF), following the 75-pulse-irradiation with the KrF laser at 124mJ/cm2 and RTA, showed a smooth surface (σRMS = 1.8 nm) and maximum blueshift of 74 nm achieved without removal of the SiO2 layer.
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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".