On MoS<sub>2</sub> Thin-Film Transistor Design Consideration for a NO<sub>2</sub> Gas Sensor
Bibliographic record
Abstract
MoS 2 thin-film transistors (TFTs) are fabricated and simulated to explore the NO 2 gas sensing mechanism depending on different device structures. In particular, the role of the Al 2 O 3 passivation layer on the MoS 2 channel has been investigated. In the case of nonpassivated MoS 2 TFTs, increase of off-current is observed with NO 2 gas, which has been modeled with the modulation of the effective Schottky barrier height for holes because of the generation of in-gap states near the valence band as NO 2 gases interact with the MoS 2 channel. The nonpassivated MoS 2 TFTs are simulated based on nonequilibrium Green’s function method, and the simulation results do confirm this sensing mechanism. On the other hand, MoS 2 TFTs with the Al 2 O 3 passivation layer have been modeled with a pseudo-double gate structure as NO 2 gases on the capping layer can act like the secondary gate inducing the positive charge state. Our quantum transport simulation shows that the significant threshold voltage shift can be achieved with NO 2 gas, which matches the experimental observation, thereby exhibiting a completely different sensing mechanism of the passivated device from the nonpassivated counterpart. In addition, we also discuss competing device parameters for the passivated MoS 2 TFTs by varying the main and the secondary gate dielectric, suggesting co-optimization to realize high sensitivity and low power consumption simultaneously.
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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) | 0.001 | 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 | 0.001 | 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".