Leakage and Charge Injection Optimization in a-Si AMOLED Displays
Bibliographic record
Abstract
In this paper, we examine the effect of switch thin-film transistor (TFT) leakage and charge injection on the operation and driving of amorphous silicon (a-Si) active matrix organic light-emitting diode (AMOLED) displays. Charge injection causes an undesirable and immediate drop in the data voltage stored on the storage capacitor CS when the switch TFT is turned off, and the leakage of the switch TFT causes the charge on CS to gradually leak out over the frame time. While making the row line negative helps reduce the leakage, it increases the voltage swing on the row line and causes more charge injection. We have demonstrated that for a given VDD, there is an optimal negative gate drive voltage on the switch TFT that minimizes the overall drop in data voltage on CS over the frame time. In addition, we have also shown that even though this optimal driving point changes with aging of the display since both leakage and VTincrease over time, it is possible to keep the voltage drop on CSconstant irrespective of aging. The analysis provides the designer with a means to improve the long term grey-scale performance of the AMOLED display
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".