Enhanced blue sensitivity in ITO/a-SiNx:H/a-Si:H MIS photodetectors
Bibliographic record
Abstract
We report an ITO/a-SiNx:H/a-Si:H MIS photodetector with improved performance in terms of its dark current, stability, and spectral response in the blue region. The a-Si:H and a-SiNx:H thin film layers were deposited by plasma-enhanced chemical vapor deposition (PECVD) on a glass substrate with patterned Mo back contact. The ITO was polycrystalline with a wide band gap (>3.75 eV) and was deposited at room temperature by magnetron sputtering. SIMS (Secondary Ion Mass Spectrometer) measurements show that an ultra thin a-SiNx:H film (a few nm) can effectively block the diffusion of oxygen from the ITO to the a-Si:H. In addition, the insulator layer provides a barrier for electrons, which serves to reduce the dark current. This is in contrast to the ITO/a-Si:H Schottky photodiode whose electrical and optical performance is impaired by the large defect density at the interface due to impurity diffusion from the ITO layer. At a reverse bias of 1 V, the dark current density of the MIS photodetector is as low as 4 nA/cm2. Photoresponse measurements show a dramatically enhanced sensitivity in the UV/blue spectral region. A high quantum efficiency (∼80%) is achieved at a wavelength of 440 nm, which can be attributed to reduction of both optical and recombination loses by virtue of the highly transparent polycrystalline ITO and the low defect density at the a-SiNx:H/a-Si:H interface.
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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 | 0.000 |
| 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".