Impact of Contact Resistance on the fT and fmax of Graphene vs. MoS2 Transistors
Bibliographic record
Abstract
A key challenge in making 2-D materials viable for electronics is reducing the contact resistance ρCof the source and drain, which can otherwise severely curtail performance. We consider the impact of contact resistance on the performance of transistors made with single-layer graphene and MoS2, two of the most popular 2-D materials presently under consideration for radiofrequency (RF) applications. While our focus is on the impact of ρC, we include the impact of all the device parasitics. We consider a device structure based on the 7-nm node of the ITRS and use the unity-current-gain and unity-power-gain frequencies (fTand fmax) found from quantum-mechanical simulations, ballistic for graphene and with scattering for MoS2, as indicators of RF performance. We quantify our results in terms of the values of ρCneeded to reach specific values of fTand fmax. In terms of peak performance (over all bias conditions), we show that graphene retains a significant edge over MoS2, despite graphene's poor output conductance, with MoS2only being able to bridge the gap if considerably better contact resistances can be realized. However, with the bias current restricted to a technologically relevant value, we show that graphene loses much of its advantage, primarily due to a reduction in its transconductance gm, and we show that MoS2can then meet or exceed the performance of graphene via the realization of contact resistances already achieved in multilayer structures. Our values of fTfor short-channel devices (around the 7-nm ITRS node) are shown to be consistent with experimental data for present-day long-channel devices, supporting our approach and conclusions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".