Rethinking the Characterization of Nanoscale Field‐Effect Transistors: A Universal Approach
Bibliographic record
Abstract
Standard methods for calculating transport parameters in nanoscale field-effect transistors (FETs), namely carrier concentration and mobility, require a linear connection between the gate voltage and channel conductance; however, this is often not the case. One reason often overlooked is that shifts in chemical and electric potential can partially compensate each other, commonly referred to as quantum capacitance. In nanoscale FETs, capacitance is often unmeasurable and an analytical formula is required, which assumes the conducting channel as metallic and common methods of determining threshold voltage no longer couple properly into transport equations. As present and future FET structures become smaller and have increased channel-gate coupling, this issue will render standard methods impossible to use. This work discusses the validity of common methods of characterization for nanoscale FETs, develops a universal model to determine transport properties by only measuring the threshold voltage of an FET and presents a new parameter to easily classify FETs as either quantum capacitance-limited or metallic approximated charge transport. Also considered in this work is electrical hysteresis from trap states and, in combination with the proposed universal model, novel techniques are introduced to measure and remove the errors associated with these effects often ignored in literature.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".