Plasma induced damage on AlGaN/GaN heterostructure during gate opening for power devices
Bibliographic record
Abstract
During the fabrication of metal oxide semiconductor high electron mobility transistor based on AlGaN/GaN heterostructure, gate patterning is recognized as the most critical step that can lead to electrical degradation of the transistor. In this work, we performed the SiN cap layer plasma etching processes by two fluorine-based plasma processes (SF6/Ar and CHF3/CF4/Ar) with low (≈15 eV) and high (≈260 eV) ion energies. Moreover, we investigate the postetching treatment using a KOH solution in order to restore the quality of the AlGaN barrier surface after etching. The objective of this article is to evaluate the AlGaN barrier surface damage after the listed plasma etching processes and postetching strategies by using quasi-in situ angle-resolved x-ray photoelectron spectroscopy, transmission electron microscopy, and atomic force microscope. Accordingly, it is found that both high ion energy plasma processes lead to a significant stoichiometric change and modification of the AlGaN barrier layer into a 1.5 nm F-rich AlGaNFx subsurface reactive layer. The decrease in ionic energy leads to a decrease in the SiN etch rate and a significant improvement in the SiN/AlGaN etch selectivity (which becomes infinite) for both plasma chemistries. Moreover, the decrease in ion energy decreases the depth of the modification (about 0.5 nm) and reduces the stochiometric change of the AlGaN barrier layer. However, both low and high ion energy SF6/Ar plasma lead to 0.8 eV Fermi level shift toward the valence band. Furthermore, the KOH postetching treatment demonstrates complete and effective removal of the AlGaNFx subsurface reactive layer and restoration of the surface properties of the AlGaN layer. However, this removal leads to AlGaN recesses that are correlated to the thickness of the reactive layer formed during the etching.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".