Realization of Low-Power Digital Circuits With Unipolar TFTs on Flexible Substrate
Bibliographic record
Abstract
There is growing interest in low-cost, low-thermal-budget electronics, particularly for displays, flexible body sensors, and affordable IoT devices. The potential of thin-film transistors (TFTs) in enabling these large-area, low-cost electronics has been proven. However, implementing complex circuits and on-chip SRAM with TFTs, which often lack complementary transistor types, poses challenges due to limited output swing, and excessive direct path current that leads to high power consumption. This paper introduces digital circuit designs that address these challenges. As a proof of concept, several key building blocks such as primary logic gates, decoders, and SRAM cells were fabricated using only n-type amorphous silicon (a-Si:H) TFTs on a glass and flexible substrate, and the impact of bending on circuit robustness was examined. The measurement results indicate that the proposed 2-to-4 decoder circuit maintains full output swing and reduces total average power consumption by 20.8compared to the state-of-the-art bootstrap circuit. Furthermore, the proposed SRAM cell reduces static power consumption by approximately 56× compared to a conventional 6T SRAM cell with unipolar TFTs.
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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".