Manufacturing of quantum-tunneling MIM nanodiodes via rapid atmospheric CVD in terahertz band
Bibliographic record
Abstract
Abstract Quantum-tunneling metal–insulator-metal (MIM) diodes have emerged as a significant area of study in the field of materials science and electronics. Our previous work demonstrated the successful fabrication of these diodes using atmospheric pressure chemical vapor deposition (AP-CVD), a scalable method that surpasses traditional vacuum-based methods and allows for the fabrication of high-quality Al2O3 films with few pinholes. Here, we show that despite their extremely small size 0.002 µm2, the MIM nanodiodes demonstrate low resistance at zero bias. Moreover, we have observed a significant enhancement in resistance by six orders of magnitude compared to our prior work, Additionally, we have achieved a high responsivity of 9 AW−1, along with a theoretical terahertz cut-off frequency of 0.36 THz. Our approach provides an efficient alternative to cleanroom fabrication, opening up new opportunities for manufacturing terahertz-Band devices. The results of our study highlight the practicality and potential of our method in advancing nanoelectronics. This lays the foundation for the development of advanced quantum devices that operate at terahertz frequencies, with potential applications in telecommunications, medical imaging, and security systems.
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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".