Fine-Tuned <i>V</i> <sub>th</sub> for Logic Reconfigurability in Dual-Gate Zinc Oxynitride Transistors
Bibliographic record
Abstract
Dual-gate transistors enable tunable electrical behavior by introducing a second gate electrode, offering enhanced control over channel formation and threshold voltage ( V th ). Here, we report a dual-gate-controlled ZnON thin-film transistor (DGC-TFT) capable of finely modulating V th (Δ V = 0.34 to 0.54 V) through independent top and bottom gate inputs. By characterizing the transfer and output curves under various bias combinations, we reveal systematic V th shifts and dual-channel switching behavior arising from asymmetric gate dielectrics, organic parylene (top) and SiO 2 (bottom). Finite-element TCAD simulations provide insights into the potential profiles, carrier distributions, and gate coupling mechanisms governing this behavior. Through this precise gate control, we demonstrate a dynamically tunable complementary inverter and implement five distinct logic functions (inverter, AND, OR, NAND, and NOR) using a single DGC-TFT device. These results highlight the promise of dual-gate architectures for compact, reconfigurable logic systems and adaptive circuit 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".