n-Type Ohmic contact and p-type Schottky contact of monolayer InSe transistors
Bibliographic record
Abstract
Owing to their few lateral dangling bonds and enhanced gate electrostatics, two-dimensional semiconductors have attracted much attention for the fabrication of channels in next-generation field-effect transistors (FETs). Herein, combining first-principle band structure calculations with more precise quantum transport simulations, we systematically explore the interface properties between monolayer (ML) indium selenide (InSe) and a sequence of common electrodes in an FET. The ML InSe band structure is damaged by Sc, Au, Cr, Pt, and Pd electrodes but identifiable in contact with Ag, Cu, In, graphene and ML O-terminated Cr2C electrodes. A lateral n-type Schottky contact is generated with Sc, Au, Cr, Pt, Pd, and ML graphene electrodes owing to Fermi level pinning originating from the metal-induced gap states, which feature a pinning factor of 0.32. Luckily, a highly desirable lateral n-type Ohmic contact is generated with the Ag, Cu, and In electrodes. The calculated contact polarity is in agreement with the available experimental results using Au, Cr, ML graphene, Ag, and In as electrodes. Remarkably, a lateral p-type Schottky contact is generated with ML O-terminated Cr2C despite the very high work function of ML InSe. Therefore, this study offers a deeper understanding of ML InSe device interfaces and instructions for the design of ML InSe transistors.
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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".