Self-Consistent Extraction of Mobility and Series Resistance: A Hierarchy of Models for Benchmarking Organic Thin-Film Transistors
Bibliographic record
Abstract
Organic thin-film transistors (OTFTs) are usually benchmarked across different devices and technologies based on the threshold voltage, charge carrier mobility, and series resistance. However, conventional parameter extraction established for silicon transistors frequently lead to misleading results when applied to OTFTs. Some of the peculiarities of OTFTs can be addressed by the virtual-source (VS) emission–diffusion (ED) theory. Using published data and own measurements, we show that the electrical characteristics of OTFTs partially limited by mobile charge supply to the channel, thermic emission, or by velocity saturation can be successfully predicted by an organic VSED model. Simplifying the current-voltage dependence obtained from the VSED theory modified for organic materials (OVSED), we introduce a hierarchy of benchmark models only employing one additional parameter with a specific electrical signature for each of these limitations. As a special case, the hierarchy includes the widely applied Shichman–Hodges (SH) model. In combination with standard nonlinear least-squares solvers, such benchmark models can replace conventional extraction methods and are applicable to a wider range of OTFT technologies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".