Dimensional scaling of high-speed printed organic transistors enabling high-frequency operation
Bibliographic record
Abstract
Abstract Printed electronics has promised to deliver low-cost, large-area and flexible electronics for mass-market applications for some time; however, so far one limiting factor has been device performance. Over the last decade, great progress has been made in terms of materials, processing and printing resolution for printed transistors. In this article, we review dimensional scaling of printed organic thin-film transistors, which has enabled high-frequency operation. We review different device architectures that require different dimensions to be scaled with accompanying tradeoffs in performance and complexity. Various printing methods have been used to print scaled transistors. Inkjet and gravure printing have seen the greatest improvements. We will focus on gravure printing here as it not only enables high-resolution features but also high-speed printing for low-cost manufacturing. Operating voltage has been scaled down less aggressively due to difficulties with scaling down the thickness of printed gate dielectrics. The performance of organic semiconductor materials has also improved substantially. When processing the semiconductor, the scaling of other device dimensions needs to be considered to optimize performance. Based on these advances, transistor switching frequency has increased dramatically over the last decade with several reports of high-speed printed inverters operating at high kHz to low MHz frequencies, which are promising results for emerging applications of printed electronics.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".