A Multi-Level Simulation of GeH FETs: From Nanomaterial and Device Characteristics to Circuit Performance Optimization
Bibliographic record
Abstract
Here, we demonstrate a multi-level simulation for 2D material-based nanoelectronics, including material parameterization, device simulation, physics-based compact modeling, and circuit benchmark. We perform quantum transport simulations based on the Non-equilibrium Green's Function (NEGF) method to calculate the characteristics of two-dimensional (2D) GeH field-effect transistors (FETs). We have developed a compact model by modifying the original virtual source (VS) model to capture the unique behaviors of 2D-material FETs such as voltage-dependent VS velocity and quantum capacitance. HSPICE circuit simulation is then conducted for circuit analyses and optimization of CMOS digital benchmark circuits. Our simulation results show that energy-delay product can be lowered by 50 times if power supply and threshold voltages are properly engineered. This study not only provides a seamless multi-level simulation process to fill a gap between the properties of nanomaterials and the behavior of circuits based on novel FETs, but also advances in-depth understanding of material, device and circuit in a comprehensive manner. It is expected that the suggested approach could be further extended to a framework for 2D material-device-circuit co-optimization processes.
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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 | 0.000 | 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".