Quantum Transport Simulations of Sub-60-mV/Decade Switching of Silicon Cold Source Transistors
Bibliographic record
Abstract
A steep-slope switch of silicon (Si) transistor with a “cold” source (CSFET) with a metal between p-type and n-type Si (pSi–M–nSi) is investigated by quantum transport simulations, which breaks the subthreshold swing (SS) limit by manipulating the density of states (DOS) of injected carriers. The injected current from the junction of pSi–gold (Au)–nSi is calculated to be over$10^{{3}}~ {\mu } \text {A}/{\mu } \text {m}$by first-principles quantum-transport simulations. Then, Si CSFETs are investigated and compared with conventional FETs and tunneling FETs. It is demonstrated that SS reaches 23 mV/decade in 15-nm Si CSFETs in the ballistic limit. ON-state current is as large as${7}.{9} \times {10}^{{2}}\,\, {\mu } \text {A}/ {\mu } \text {m}$at${V}_{D} = {0}.{5} \text {V}$with${I}_{\text {off}}$fixed at$10~\text {pA}/ {\mu }\text {m}$. The SS of CSFETs degrades with decreasing gate length and cannot be smaller than 60 mV/decade at${L} _{G}$= 6 nm. CSFETs have temperature-independent SS due to the cold electron injection, which is different from FETs and Dirac source FETs. The output characteristics demonstrate that CSFETs exhibit negative differential resistance and can achieve current rectification. Finally, the effects of cold electron rethermalization due to electron–phonon scattering in 15-nm CSFETs are found to degrade the SS to 50 mV/decade in six orders of magnitude of drain current. The SS can be improved to 42 mV/decade by using shorter lengths of the metal layer and n-type Si in the source to minimize the scattering.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".