Numerical Simulations of Space Charge Waves Amplification Using Negative Differential Conductance in Strained Si/SiGe at 4.2 K
Bibliographic record
Abstract
This paper introduces a two-dimensional (2D) numerical simulation of the amplification of space charge waves using negative differential conductance in a typical MOS silicon–germanium (SiGe)-based field-effect transistors (FET) and complementary metal oxide semiconductor (CMOS) technology at 4.2 K. The hydrodynamic model of electron transport was applied to describe the amplification of space charge waves in this nonlinear medium (i.e., the negative differential conductance). This phenomenon shows up in GaAs thin films at room temperature. However, this can be also observed in a strained Si/SiGe heterostructure at very low temperatures (T < 77 K) and at high electric fields (E > 10 KV/cm). The results show the amplification and non-linear interaction of space charge waves in a strained Si/SiGe heterostructure occurs for frequencies up to approximately 60 GHz at T = 1.3 K, 47 GHz at T = 4.2 K, and 40 GHz at T = 77 K. The variation of concentration and electric field in the Z and Y directions are calculated at 4.2 K. The electric field in the Z direction is greater than in the Y direction. This is due to the fact that this is the direction of electron motion. In addition to deep space applications, these types of devices have potential uses in terrestrial applications which include magnetic levitation transportation systems, medical diagnostics, cryogenic instrumentation, and superconducting magnetic energy storage systems.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".