Unraveling the Influence of Surface Contaminants and Cleaning Protocols on Charge States of SiO<sub>2</sub> Dielectrics and the Performance of Organic Field-Effect Transistors
Bibliographic record
Abstract
Organic field-effect transistors (OFETs) fabricated on SiO 2 /Si wafers represent a crucial avenue for the development of organic-on-silicon technology and serve as a platform for materials science to characterize carrier mobility. The surface cleaning treatment of wafers is critical because OFETs are highly sensitive to surface states. In this study, the effects of intrinsic organic contaminants on wafers, as well as the functions of commonly used cleaning solvents and treatments on the surface states, electronic structure of SiO 2, and the performance of OFETs were systematically investigated. The intrinsic organic contaminants on the wafer surface were identified as being in positively charged states, which led to high operational voltages of OFETs and degraded bias-stress stability. However, their impact on field-effect mobility was found to be minimal following any surface cleaning process. The conventional ultrasonic cleaning using acetone and isopropanol only partially removed contaminants, which provides limited improvement in reducing operational voltages and enhancing bias-stress stability. The subsequent ultrasonic cleaning with deionized water and UV-ozone treatment further removed contaminants. More importantly, these processes anchor hydroxyl species on the SiO 2 surface, which impart negative charges for the neutralization of surface charge states, thereby comprehensively enhancing operational performance of devices. By leveraging the distinct properties of various solvents and treatments, an optimized surface cleaning protocol was proposed, in which the threshold voltage of OFETs was reduced to nearly 0 V, and the bias-stress stability was enhanced by approximately 300%.
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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".