Influence of Glass Surface Modification on Thin Film Copper Electrodes
Bibliographic record
Abstract
Large-area and ultra-high resolution displays continue to progress driven by new performance requirements and applications. As display sizes become larger, the industry demands metal electrodes with low resistance to reduce the gate delay time and signal distortion. Cu is an attractive candidate due to its relative low resistivity and its superior resistance to electromigration as opposed to materials such as Al or Mo. For bottom gate TFT technologies, the metal electrode is deposited directly on the glass substrate. Cu is known to have poor adhesion with oxide materials such as the substrate glass. Typically, an adhesion layer such as Ti, Mo, or other metal/alloy is required to improve the adhesion of Cu to the substrate. In this work, we modify Corning® Eagle XG® glass surfaces by various treatments to study the impact of the glass surface on the structural and electrical properties of Cu. Ti was used the adhesion layer in this study. Atomic force microscopy is used to examine the surface topography and the surface roughness of Cu. In-plane X-ray diffraction results characterize the crystallization and crystalline in-plane orientation of Cu. Transmission electron microscopy is used to further investigate the structure of the Ti and Cu films. The films properties are correlated with 4-point probe measurement of the sheet resistance. The film adhesion performance is determined using the tape test. The role of the glass surface state is demonstrated through its impact on the structural and electrical properties of Ti/Cu electrodes emanating from glass surface treatments.
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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".