Spin‐coating fabrication of high‐yield and uniform organic thin‐film transistors via a primer template growth
Bibliographic record
Abstract
Abstract Solution coating of organic semiconductors offers great potential for achieving low‐cost and high‐throughput manufacturing of large‐area and flexible electronics. However, the solution processability of semiconducting small molecules for fabricating uniform and reliable thin‐film devices poses challenges due to the low viscosities of small‐molecule solutions. Here, we report a universal approach employing a primer template (PT) to enhance the spreadability of small‐molecule solutions on silicon wafers, enabling the spin‐coating fabrication of uniform thin films composed of millimeter‐scale grains with complete large‐area coverage and well‐ordered molecular packing. Using PT, we fabricated organic thin‐film transistors (OTFTs) using solutions containing various small molecules such as rubrene and 2‐decyl‐7‐phenyl‐[1]benzothieno[3,2‐b][1]benzothiophene. The device yield of all fabricated OTFTs is consistently 100% while achieving a high average mobility of 1.62 cm2 V−1 s−1 with a device‐to‐device variation of 7.7% measured in ambient air condition. In addition, the utilization of PT resulted in a batch‐to‐batch variation of 12.5% in device performance over dozens of OTFT devices. The key industrial manufacturing metrics, such as device yield, reproducibility, and performance uniformity of the PT OTFTs, are among the best for devices fabricated using solution spin‐coating techniques.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".