p-GaN Contact Study by Means of Electrochemical Short Loop
Bibliographic record
Abstract
Surface states and contamination affects the efficiency of Gallium Nitride (GaN) based devices such as HEMTs, LEDs, photodetectors etc. In this work, several wet chemical treatment has been performed for the surface treatment of p-GaN, in order to achieve low-resistance ohmic contacts. The effect of surface treatment is observed by studying the flatband potential across the p-GaN/electrolyte interface via, electrochemical characterization. The electrochemical method of “Mott-Schottky characterization” gives simple and quick way of analysing the effect of different treatments, without the need of device fabrication. The effect of surface treatment are further investigated via, X-ray Photoelectron Spectroscopy and Atomic Force Microscopy. The potential surface treatments from experimental findings are integrated into device processing steps in order to realize effect of surface treatment on the p-GaN/metal contacts. The electrochemical characterization shows that HCl results in least flatband potential. Further, The electrical test shows that HCl treatment results in significant orders of magnitude improvement in contact resistance and electrical performance of the devices. This shows that short loop electrochemical characterization can be used for predicting surface treatment in order to improve electrical characteristics of the device.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".