Enhanced Organic Thin-Film Transistor Stability by Preventing MoO<sub>3</sub> Diffusion with Metal/MoO<sub>3</sub>/Organic Multilayered Interface Source-Drain Contact
Bibliographic record
Abstract
The source-drain electrode with a MoO 3 interfacial modification layer (IML) is considered the most promising method to solve electrical contact issues impeding organic thin-film transistors (OTFTs) from commercialization. However, this method raises many concerns because MoO 3 might diffuse into organic materials, which causes device instability. In this work, we observed a significant device stability degradation by damaging on/off switching performance caused by MoO 3 diffusion. To prevent the MoO 3 diffusion, a source-drain electrode with a multilayered interface contact (MIC) consisting of a top-down stack of metal, MoO 3 IML, and organic buffer layer (OBL) is proposed. In the MIC device, the MoO 3 IML serves well for its intended functions of reducing contact resistance and suppressing minority carrier injection to the OTFT channel. The inclusion of OBL to the MIC helps block MoO 3 diffusion and thereby leads to better device stability and an increased on/off ratio. Through combinations with several organic compounds as a buffer layer, the MoO 3 diffusion related electrical behaviors of OTFTs are systematically studied. Key parameters related to MoO 3 diffusion such as the Fick coefficient and bias-stress stability such as carrier trapping time are extracted from numerical device analysis. Finally, we summarize a general rule of material selection for making robust source-drain contact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".