Ballistic Transport in State-of-the Art In<sub>0.65</sub>Ga<sub>0.35</sub>As/In<sub>0.52</sub>Al<sub>0.48</sub>As Quantum-Well High-Electron-Mobility Transistors at Room and Cryogenic Temperatures
Bibliographic record
Abstract
This paper presents a systematic analysis of ballistic transport in state-of-the-art$\text{In}_{0.65}\text{Ga}_{0.35}\text{As}$QW HEMTs at both room and cryogenic temperatures. We examine devices with a wide range of gate lengths from 300 nm to sub-100 nm, to study near-ballistic transport, particularly in terms of ballistic mobility and mean-free-path, especially at 4 K. We also consider the impact of channel carrier degeneracy in the$\text{In}_{0.65}\text{Ga}_{0.35}\text{As}$QW channel layer, which arises from the relatively low density of states in this layer. Our work underscores the significant role of ballistic mobility in state-of-the art$\text{In}_{0.65}\text{Ga}_{0.35}\text{As}$HEMTs from 300 K to 4 K. It reveals that quantum-mechanical ballistic transport and channel degeneracy must be accounted for during device modeling and characterization. Furthermore, we propose a methodology to graphically determine whether carrier transport is governed by conventional diffusive transport or near-ballistic transport in terms of channel carrier degeneracy and$L_{g}$at each temperature ranging from 300 K to 4 K.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".