Impact of PECVD deposition on dielectric charge and passivation for n-GaN/SiO<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si17.svg" display="inline" id="d1e1552"><mml:msub><mml:mrow/><mml:mrow><mml:mi>x</mml:mi></mml:mrow></mml:msub></mml:math> interfaces
Bibliographic record
Abstract
Controlling properties of GaN/dielectric interfaces is crucial for determining the characteristics of MOS-HEMT devices and their stability. Interface properties are largely affected by the techniques and specific conditions of dielectric deposition. In this work, a Taguchi design of experiment was applied to study the effect of plasma parameters during deposition of SiO x by PECVD for passivation of n-GaN. SiO x /GaN MIS capacitors were fabricated and characterized by capacitance measurement at a high probing frequency of 1 MHz. The interface states density, hysteresis and flatband voltage were analyzed and modeled in relation with the flow of SiH 4 , plasma power, chamber pressure and temperature. Excellent fits could be obtained on a single model including linear terms for all studied parameters and quadratic terms for the flow of SiH 4 and temperature. We show that it is possible to obtain some control of the flatband voltage while maintaining a good interface quality. Positive flatband voltages are potentially of interest to enable normally-off operation for MOS-HEMTs and this could be obtained mainly by using a high SiH 4 /N 2 O ratio. To the contrary, negative flatband voltage values often ensure the most stable operation of MOS-HEMTs and this was achieved with a low SiH 4 /N 2 O and high plasma power. MIS capacitors with near-zero flatband voltage were also obtained with low SiH 4 /N 2 O ratio and low plasma power. Hysteresis and interface states density in relation with deposition plasma conditions are also analyzed in order to offer the best trade-offs depending on the end applications of MOS-GaN devices. By demonstrating the great impact of plasma conditions during dielectric deposition on electronic properties of MIS devices, we show that the process of gate insulation can be optimized to simultaneously control the density of defects and fixed charge at the interface. • Taguchi DOE is applied to GaN passivation by PECVD SiO x . • Interface properties in response to PECVD parameters are modeled. • Minimization of interface defects and flatband voltage control is demonstrated. • Fixed interface charge quantified and shown to vary in the range −1 × 10 12 to 1 × 10 12 cm −2 .
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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 | 0.001 |
| 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".