Study of electrical properties of Al/Si<sub>3</sub>N<sub>4</sub>/<i>n</i>-GaAs MIS capacitors deposited at low and high frequency PECVD
Bibliographic record
Abstract
As for silicon, surface passivation of GaAs and III-V semiconductors using silicon nitride (Si 3 N 4 ) deposited by plasma enhanced chemical deposition (PECVD) is widely used to improve devices and circuits stability, reliability and for encapsulation. In this work, the effect of plasma excitation frequency in the PECVD reactor on the surface passivation efficiency of GaAs during Si 3 N 4 deposition was investigated. Metal-Insulator-Semiconductor (Al/Si 3 N 4 / n -GaAs) capacitors are fabricated and characterized using capacitance–voltage ( C – V ), and conductance–voltage ( G – V ) to compare electronic properties of GaAs/Si 3 N 4 interfaces depending on the use of a high frequency PECVD (HF-PECVD) or low frequency (LF-PECVD) process. The drastic advantage of using the LF-PECVD technique for the passivation of GaAs is clearly demonstrated on the characteristic C – V at 1 MHz where a good surface potential was observed, while a quasi-pinned surface Fermi level was found when HF-PECVD was used. To unpin Fermi level, a sulfur pre-treatment prior before HF-PECVD deposition and post-metallisation annealing were necessary. A lower frequency dispersion and a lower hysteresis indicating low densities of slow traps were observed for MIS devices fabricated by LF-PECVD. The advantage of having an efficient passivation without sulfur treatment is important since ammonium sulfide used for this purpose is corrosive and difficult to adapt in industrial environment. The better electronic properties of GaAs/Si 3 N 4 interface were found for silicon nitride layers using LF-PECVD deposition. This can probably be associated with the high-level injection of H + ions on the semiconductor surface reducing thus the native oxides during the initial steps of dielectric deposition.
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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".