Improved Circuit Model Fitting of Inkjet-Printed OTFTs and a Proposal for Standardized Parameter Reporting
Bibliographic record
Abstract
Within the field of organic thin-film transistors (OTFTs), a large variety of materials are available. As a consequence, it may not be possible for one circuit model to accurately replicate the behavior of all OTFT devices. We propose modifications to two popular circuit models in order to better match the characteristics observed in the devices manufactured using our materials system. Using measured data for complementary n- and p-type organic devices, modeling parameters are extracted through optimization. Due to the ubiquitous use of the square-law model to characterize organic devices, modeling parameters are also extracted using this traditional approach. Extracted parameters are then compared and discussed. As there is a clear variation in device parameters based on the model used, a new standardization scheme is proposed which attempts to provide a standardized quality assurance metric, which simplifies the comparison of the reported device parameters. This scheme provides an indication of the goodness of fit between the model being used to describe the device and the extracted modeling parameters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".