Simulated electron affinity tuning in metal-insulator-metal (MIM) diodes
Bibliographic record
Abstract
Metal-insulator-metal diodes for rectification applications must exhibit high asymmetry, nonlinearity, and responsivity. Traditional methods of improving these figures of merit have consisted of increasing insulator thickness, adding multiple insulator layers, and utilizing a variety of metal contact combinations. However, these methods have come with the price of increasing the diode resistance and ultimately limiting the operating frequency to well below the terahertz regime. In this work, an Airy Function Transfer Matrix simulation method was used to observe the effect of tuning the electron affinity of the insulator as a technique to decrease the diode resistance. It was shown that a small increase in electron affinity can result in a resistance decrease in upwards of five orders of magnitude, corresponding to an increase in operating frequency on the same order. Electron affinity tuning has a minimal effect on the diode figures of merit, where asymmetry improves or remains unaffected and slight decreases in nonlinearity and responsivity are likely to be greatly outweighed by the improved operating frequency of the diode.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".