Advancements in Inkjet Printing Techniques for Improving Field-Effect Transistors in Printed Electronics
Bibliographic record
Abstract
This thesis centers on the enhancement of printed field-effect transistors (FETs) with a primary focus on addressing challenges through innovative inkjet printing methodologies. The study concentrates on the utilization of hydrophobic fluoropolymers, particularly Teflon amorphous fluoropolymer (Teflon-AF), as gate dielectrics in organic thin-film transistors (OTFTs). Teflon-AF, while promising for its low charge trap density, thermal stability, and low dielectric constant, presents impediments due to its low surface energy, resulting in issues like dewetting and bulging of printed patterns. This obstructs the creation of uniform lines using low-viscosity ink. To resolve these challenges, this thesis presents novel strategies for inkjet printing micro-patterns on hydrophobic surfaces. Two approaches are outlined to successfully inkjet print micro-patterns on hydrophobic surfaces. The first involves a sequential inkjet printing and drying process, which maintains ink adherence to the surface. Meanwhile, an energy minimization technique predicts the equilibrium shape and volume of patterns, which is influenced by surface tension forces. The simulation accurately predicts the required ink volume for achieving dry patterns with smooth edges, advancing inkjet printing techniques for electronics. The second approach demonstrates stacked-coin methodology to form smooth lines on hydrophobic surfaces. Variations in drop spacing, stage speed, and stage temperature map out various regimes: isolated droplets, isolated groupings, broken lines, true stacked-coin, and delamination. The study further investigates the fabrication of OTFTs employing Teflon-AF gate dielectric. Plasma treatment is applied to render the hydrophobic layer amenable to inkjet printing of silver electrodes. Morphological and surface chemical properties of Teflon-AF films change after plasma treatment and gradually reverse after annealing of the electrodes. This affects the organic semiconductor/dielectric interface and, consequently, the OTFT performance. Moreover, the research explores inkjet printing capabilities by incorporating carbon nanotubes (CNTs) into the TFT channel. Strategic utilization and engineering of the coffee ring effect in inkjet-printed CNT channel obviate the need for surface treatment. Inkjet printing leads to the aligning and bundling of CNTs on the line edges, which improves device performance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".