A Low Static-Power D Flip-Flop With Unipolar Thin Film Transistors on a Flexible Substrate
Bibliographic record
Abstract
There is increasing interest in affordable and flexible electronics, driven by the need for displays, conformable body sensors, and internet-of-things (IoT) gadgets. Amorphous silicon (a-Si:H), transition metal oxides, and organic thin-film transistors (TFTs) have demonstrated cost-effective large-scale production. As TFTs are typically unipolar in nature, they pose challenges for implementing CMOS-like circuits. Conventional methods to realize circuits in these technologies often lead to restricted voltage swing and excessive direct path current. While several methods have been proposed to counter the voltage swing issue, these methods fail to address the direct path current problem. This article presents low static-power D flip-flops (DFFs) using unipolar TFTs, which significantly reduces the direct path current. The proposed and conventional DFF designs were fabricated on a glass and flexible substrate using a-Si:H TFTs. Additionally, the impact of bending the flexible substrates was examined to assess the robustness and performance of the DFFs under mechanical strain. The measurement results show that the proposed design based DFF saves average total power by 79.8% compared to conventional design.
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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 | 0.001 | 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".