Flexible Low-Power Digital Circuits With Unipolar Amorphous Silicon Thin-Film Transistors
Bibliographic record
Abstract
Thin-film transistor (TFT) technology has demonstrated its effectiveness in large-area cost-efficient applications such as displays, flexible electronics, and medical devices. However, TFTs are typically unipolar in nature, and therefore, the realization of CMOS-like digital circuits is challenging. Traditional methods for implementing logic gates and complex circuits with unipolar TFT devices lead to high static power consumption and limited output swing. While various mitigation techniques have been developed, they fail to eliminate the direct path current problem in these circuits, which hinders static power reduction. The objective of this study is to address these issues and study its effect on flexible substrate.In this article, we propose logic gates that address these issues using a half-latch circuit. To demonstrate the concept, a 3-to-8 decoder was built using only n-type amorphous silicon (a-Si:H) TFTs on both glass and flexible substrates. We analyzed the impact of bending and substrate materials on the design. It was observed that the TFTs show an increase in current up to 8% under tensile stress, while a decrease in current up to 4% under compressive stress on flexible substrate. Measurements indicate that the proposed design reduces the average total power consumption of the 3-to-8 decoder by 46.5% compared to state-of-the-art techniques under various conditions.
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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".