Compact modeling of charge carrier mobility in organic thin-film transistors
Bibliographic record
Abstract
Finding the common points in theoretical models for mobility in thin-film transistors (TFTs), we demonstrate that there exists a generic analytical model for the mobility in organic TFTs (OTFTs), and the generic model is then converted into a TFT Compact Mobility Model, which is physically derivable from one perspective, and properly arranged to be suitable for compact modeling of OTFTs from another perspective, by separation and proper interfacing of temperature and bias dependence of the mobility, both significant for OTFTs, with the compact models for electrical current in OTFT. The proposed TFT Compact Mobility Model is verified theoretically and against experimental data, and the model is applicable even for high temperatures T>To, above the characteristic temperature To of the distribution of states in the organic material, a condition at which other models diverge in principle. The improvement is achieved by the identification of a temperature “shaping” function, which contains a diverging function when derived theoretically elsewhere at idealized assumptions, and we suggest an approach to remedy the problem, since divergence in characteristic equations of compact models is not allowed. However, an open question remains for the bias enhancement in mobility at high temperatures, for which case no physical model is available at present. Another essential practical feature of the TFT Compact Mobility Model is that the model is both upgradable and reducible, allowing for easier implementation, modifications and independence of characterization techniques, enabling a systematic fitting of experimental data with large scattering in the values, which is the case for OTFT nowadays.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".