2D Material-Based MVS Model and Circuit Performance Analysis for GeH Field-Effect Transistors
Bibliographic record
Abstract
This paper presents an improved multi-level simulation framework for 2D material-based nanoelectronics, which expands from device simulation, physics-based compact modeling, and circuit benchmarking, using the germanane (GeH) metal-oxide-semiconductor field-effect transistors (MOSFETs) as an example. The device simulation employs the non-equilibrium Green's function method to obtain the characteristics of 2D GeH MOSFETs for both n-type MOSFETs and p-type MOSFETs. A compact model based on the MIT virtual source model is then revised to capture the unique behaviors of 2D-material-based MOSFETs, including voltage dependency of virtual source velocity and drain-induced barrier lowering, as well as the effect of quantum capacitance. HSPICE circuit simulations are performed to analyze and optimize CMOS digital benchmark circuits. The case study demonstrates that 2D material-based transistors favor a different range of supply voltage and threshold voltage than their silicon counterpart, to achieve the optimal energy-delay product. The impact of contact resistance is also analyzed using the proposed framework. This study offers a seamless multi-level simulation approach to bridge the gap between nanoelectronics and circuit behavior, thereby advancing the understanding of materials, devices, and circuits comprehensively. The framework tailored for GeH MOSFETs provides accurate device-circuit co-optimization which can be easily extended to devices based on other 2D materials.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".