TEOS layers for low temperature processing of group IV optoelectronic devices
Bibliographic record
Abstract
The thermal budget is highly critical in processing the emerging group IV Silicon-Germanium-Tin (SiGeSn) optoelectronic devices. These emerging semiconductors exhibit a fundamental direct bandgap covering the mid-infrared range at Sn contents above 10 at. %, which is an order of magnitude higher than the equilibrium solubility. Consequently, the device processing steps must be carried out at temperatures low enough to prevent the degradation of these metastable layers. However, conventional optoelectronic device fabrication methods often require the deposition of dielectric layers at temperatures reaching 400 °C. Although this temperature can be sustained in processing a variety of devices, yet it is sufficiently high to damage GeSn and SiGeSn device structures. With this perspective, the authors investigated the morphological and optical properties of tetraethylorthosilicate (TEOS) layers as an alternative material to conventional dielectric layers. Spin-on-glass deposition on an Si wafer with baking temperatures in the 100–150 °C range leads to high homogeneity and low surface roughness of the TEOS layer. The authors show that the TEOS optical transmission is higher than 90% from visible to mid-infrared wavelengths (0.38–8 μm), combined with the analysis of the real and complex part of the refractive index. Furthermore, the TEOS deposition on GeSn and SiGeSn samples does not affect the material crystallinity or induces clustering of Sn atoms. Therefore, the low deposition temperature and high transparency make TEOS an ideal material for the integration of metastable GeSn and SiGeSn semiconductors in the fabrication of mid-infrared photonic devices.
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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".