Metal‐Insulator‐Insulator‐Metal Diodes with Responsivities Greater Than 30 A W<sup>−1</sup> Based on Nitrogen‐Doped TiO<i><sub>x</sub></i> and AlO<i><sub>x</sub></i> Insulator Layers
Bibliographic record
Abstract
Abstract Metal‐insulator‐insulator‐metal diodes based on nitrogen‐doped titanium dioxide (NTiOx) and aluminum oxide (NAlOx) are fabricated and characterized for the first time. Pt/TiOx‐NTiOx/Pt and Pt/TiOx‐NAlOx/Pt diodes with 30 nm of TiOx or NTiOx and 5 nm of NAlOx are compared to undoped Pt/TiOx/Pt and Pt/TiOx‐AlOx/Pt diodes of similar thickness. The nitrogen atoms are expected to modify the barrier heights and produce electron traps in the insulators. This changes the conduction mechanisms of the doped diodes, including the introduction of unidirectional, defect‐mediated Poole–Frenkel transport and trap‐assisted tunneling, which increase the performance of the doped diodes. The representative figures of merit observed for a Pt/TiOx‐NAlOx/Pt diode at 0.5 V include an asymmetry of 8.76 × 103, nonlinearity of 4, and zero‐bias responsivity of 22.3 A W−1. Zero‐bias responsivities as high as 36.8 A W−1 are obtained, which surpass the 19.4 A W−1 theoretical limit for Schottky diodes. Notably, this defect‐engineering approach is found to improve the figures of merit without an unwanted increase in diode resistance. A thinner Pt/NTiOx‐NAlOx/Al diode is also produced, which has a low zero‐bias resistance of 36 Ω, nonlinearity of 3.7, and zero‐bias responsivity of 1.7 A W−1.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".