Single-Technology-Based Statistical Calibration for High-Performance Active-Matrix Organic LED Displays
Bibliographic record
Abstract
Active-matrix organic light-emitting-diode (AMOLED) displays based on amorphous hydrogenated silicon (a-Si:H) thin-film transistors (TFTs) are the state of the art in display technology, owing to the feasilibility of low-cost fabrication and accessability to well-established TFT-LCD fabrication. While the a-Si:H TFT offers excellent matching of device properties over large areas, it suffers from a gate-bias-dependent threshold voltage shift in time, leading to grayscale inaccuracies. In order to counter this problem, many compensation circuits have been designed. The purpose of the compensation circuit is to estimate the threshold voltage shift in driver TFTs and apply a correction so as to maintain a constant brightness. However, all of the compensation circuits designed to date suffer from low spatial and temporal resolution and reliability issues or high cost due to the use of custom-made CMOS technology. In this paper, we focus on building AMOLED display systems solely based on a-Si:H TFT technology along with the use of off-the-shelf CMOS components to lower costs. Furthermore, we achieve high spatial and temporal resolution and high yield with the use of a two-TFT voltage programmed pixel circuit along with a statistical based external calibration circuit.
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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 | 0.000 | 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".