Bibliographic record
Abstract
Design of the VPPCs that provides the required configurability for voltage programming is hindered by several issues: complexity (a lower yield and aperture ratio), extra controlling signals (more complex external drivers), and extra operating cycles (overhead in power consumption). Moreover, the limited time provided for V T generation by the conventional addressing scheme, results in imperfect compensation. This appendix reviews different methods in increasing the V T -generation time [70, 71]. Interleaved addressing scheme The interleaved addressing scheme depicted in Figure A.1 is based on V T generation for several rows simultaneously. The rows in a panel are divided into a few segments and the V T -generation cycle is carried out for each segment. As a result, the time assigned to the V T -generation cycle is extended by the number of rows in a segment leading to more precise compensation. Particularly, since the leakage current of a-Si:H TFTs is small (of the order of 10 −14 ), the generated V T can be stored in a capacitor and be used for several other frames (see Figure A.1). As a result, the operating cycles during the following post-compensation frames are reduced to the programming and driving cycles similar to the operation of conventional 2-TFT pixel circuit [6]. Consequently, the power consumption associated with the external driver and with charging/discharging the parasitic capacitances is divided between the same few frames. In Figure A.1, the number of frames per segment is denoted as “ h ” and the number of frames per compensation interval as “ l ”. As seen, the driving cycle of each row starts with a delay of τ P from the previous row, which is the timing budget of the programming cycle. Since τ P (of the order of 10 µs) is much smaller than the frame time (of the order 16 ms), the latency effect is negligible. However, to improve the brightness accuracy, one can either change the programming direction each time, so that the average brightness lost due to latency becomes equal for all the rows, or take into consideration this effect in the programming voltage of the frames before and after the compensation cycles.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".