Black Phosphorus: New Opportunities in Electronic Device Applications
Bibliographic record
Abstract
Black phosphorus (BP) is one of layered materials drawing significant attentions in solid-state and electrochemical societies. BP has a direct bandgap, which can be tuned by varying the thickness of material or the number of layers. Due to its large carrier mobility, BP is considered as a promising contender for the future electronic device applications. However, designing field-effect transistors (FETs) based on BP is not straightforward since the relevant device physics can be significantly different from that of conventional metal-oxide-semiconductor (MOS) FETs based on 3D materials such as silicon or III-V semiconductors. Therefore, careful engineering practices are required to use BP for transistor applications. In this study, we will mainly discuss design strategies for conventional FET structures and tunnel FETs (TFETs) based on the novel layered material of BP. We perform self-consistent atomistic quantum transport simulations using non-equilibrium Green’s function (NEGF) formalism with tight-binding approximation. For high-performance device applications, conventional FET structure based on BP is considered. Our simulation results reveal that, among few-layer BPs, monolayer BP can provide the best device performance with the largest on current (~5 mA/μm), the largest on-off current ratio (~107), and the smallest subthreshold swing (62 mV/dec), showing three times larger on current and three orders of magnitude smaller off current compared to 2022 International Technology Roadmap for Semiconductors (ITRS). Although bilayer BP FETs also exhibit as comparable device performance as monolayer BP FETs, in general, thicker BP is not preferable mainly due to the worse gate electrostatic control, which affects the overall device performance negatively. Secondly, for low-power devices, BP is integrated into the lateral tunnel FET structure, where small off current and steep subthreshold slope are of great importance rather than on state characteristics. Our simulation results show bilayer and trilayer BP are preferable for TFETs applications, unlike the conventional FET structure, while monolayer BP suffers from small on current due to its large bandgap. By carefully engineering various device parameters, an ultra-high on-off current ratio (> 1011) and an extremely small subthreshold swing (~15 mV/dec) can be achieved in bilayer and trilayer BP TFETs, demonstrating the great potential of few-layer BP over monolayer. This study shows that BP FETs can be tuned for various target applications by engineering the material and device parameters properly.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".