Design strategy of two-dimensional material field-effect transistors: Engineering the number of layers in phosphorene FETs
Bibliographic record
Abstract
Thickness or the number of layers in 2D semiconductors is a key parameter to determine the material's electronic properties and the overall device performance of 2D material electronics. Here, we discuss the engineering practice of optimizing material and device parameters of phosphorene field-effect transistors (FETs) by means of self-consistent atomistic quantum transport simulations, where the impacts of different numbers of phosphorene layers on various device characteristics are explored in particular, considering two specific target applications of high-performance and low-power devices. Our results suggest that, for high-performance applications, monolayer phosphorene should be utilized in a conventional FET structure since it can provide the equally large on current as other multilayer phosphorenes (Ion > 1 mA/μm) without showing a penalty of relatively lower density of states, along with favorableness for steep switching and large immunity to gate-induced drain leakage. On the other hand, more comprehensive approach is required for low-power applications, where operating voltage, doping concentration, and channel length should be carefully engineered along with the thickness of phosphorene in tunnel FET (TFET) structure to achieve ultra-low leakage current without sacrificing on current significantly. Our extensive simulation results revealed that either bilayer or trilayer phosphorene can provide the best performance in TFET with the maximum Ion/Ioff of ∼2 × 1011 and the subthreshold swing as low as 13 mV/dec. In addition, our comparative study of phosphorene-based conventional FET and TFET clearly shows the feasibility and the limitation of each device for different target applications, providing irreplaceable insights into the design strategy of phosphorene FETs that can be also extended to other similar layered material electronic devices.
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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 | 0.001 | 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".