Near zero-bias MIIM diode based on TiO2/ZnO for energy harvesting applications
Bibliographic record
Abstract
Energy harvesting rectennas require ultrafast rectifying diodes that are efficiently matched to the optical nanoantenna. These diodes should possess low on-resistance and high responsivity. Here, we introduce a metal-insulator-metal diode composed of a new material, Ti-TiO2-Al. This diode has a 1.0 nm ultrathin insulator layer fabricated using atomic layer deposition (ALD). It has a zero-bias resistance of 275 Ω and a maximum responsivity of 3.1 A/W. To further improve its performance, another ultrathin layer of ZnO was added. The proposed Ti-TiO2/ZnO-Al metal-insulator-insulator-metal diode has a zero-bias resistance of 312 Ω and a maximum responsivity of 5.1 A/W. The two types of diodes are fabricated on a SiO2 substrate using conventional photolithography and ALD. Between 20 °C and 55 °C, the I-V characteristics did not show much temperature dependence. The effective barrier height, dielectric constant, and electron effective mass in each insulator are extracted using a constrained and derivative-based optimization algorithm.
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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".