Performance Optimization of Monolayer 1T/1T’-2H MoX<sub>2</sub> Lateral Heterojunction Transistors
Bibliographic record
Abstract
Recently, 2-D transition metal dichalcogenides (TMDs) lateral heterojunction field-effect transistors (FETs) have been demonstrated experimentally, in which metallic TMDs were used for the source/drain. In this work, we systematically investigate the contact property and device performance of monolayer 1T/1T'-2H MoS2, MoSe2, and MoTe2FETs. Schottky barrier (SB) heights are extracted from density functional theory calculations, and nonequilibrium Green's function transport simulations have been performed to study device characteristics. Our simulation results show that the inherent SB strongly affects the overall performance of these devices. Here, we optimize the performance of TMD lateral heterojunction FETs by using two different approaches. First, we have improved the electrostatic control by scaling equivalent oxide thickness and gate underlap, which boosts both ON- and OFF-state characteristics, making the device suitable for high-performance applications. On the other hand, moderate doping has been used in the gate underlap region, improving ON/OFF current ratio with negligible impacts on ON-state characteristics, and this approach is more preferred for low-power applications. This study reveals that 1T'-2H MoTe2FET shows the highest ON current (~ 1 mA/ μm) among the three with a reasonably small subthreshold swing (80 mV/dec) if properly scaled, while 1T-2H MoS2FET exhibits the highest ION/IOFF(~107) when ohmic contact is established with moderate doping in the gate underlap region. This study not only provides physical insight into the electronic devices based on novel TMD heterostructures but also suggests engineering practice for device performance optimization in experiments.
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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 | 0.001 | 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".