Benchmarking contact quality in N-type organic thin film transistors through an improved virtual-source emission-diffusion model
Bibliographic record
Abstract
Due to nonideal behavior, current organic thin film transistor technologies lack the proper models for essential characterization and thus suffer from a poorly estimated parameter extraction critical for circuit design and integration. Organic thin film transistors are often plagued by contact resistance, which is often less problematic in inorganic transistors; consequently, common models used for describing inorganic devices do not properly work with organic thin film transistors. In this work, we fabricate poly{[N,N′-bis(2-octyldodecyl)-naphthalene-1,4,5,8-bis(dicarboximide)-2,6-diyl]-alt-5,5′-(2,2′-bithiophene)} based organic thin film transistors with reduced contact resistance through the introduction of metallic interlayers between the semiconductor and gold contacts. The addition of 10 nm thick manganese interlayer provides optimal organic thin film transistor device performance with the lowest level of contact resistance. Improved organic thin film transistors were characterized using an improved organic virtual-source emission diffusion model, which provides a simple and effective method to extract the critical device parameters. The organic virtual-source emission diffusion model led to nearly perfect prediction using effective gate voltages and a gate dependant contact resistance, providing a significant improvement over common metal–oxide–semiconductor field-effect transistor models such as the Shichman–Hodges model.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".