High Photoresponsivity Multilayer MoS<sub>2</sub> Thin-Film Transistors with Local Bottom Gate Structure
Bibliographic record
Abstract
2D layered transition metal dichalcogenides (TMDs), especially MoS2, has received great attention for next-generation semiconductor devices because their thin film transistors (TFTs) show a nearly ideal subthreshold swing (SS ≈ 70 mV decade-1), high on/off current ratio (I on/I off ≈ 108), and high field-effect mobility (µ eff > 100 cm2 V-1 s-1). In addition, TMDs have the optical characteristic of tuning bandgap from direct to indirect with increasing their layers. Even though photoresponsivity of multilayer MoS2 phototransistor due to its nature of indirect bandgap is lower than that of single layer MoS2, multilayer MoS2 has higher density-of-state and wider spectral response than single layer MoS2. In order to enhance photoresponsivity of multilayer MoS2 phototransistor, we suggest the phototransistor with local bottom gate structure (LBGS), where the bottom gate length of the device is shorter than the channel length. The underlap regions of between gate and channel work like the series resistance in dark state due to ineffective gate modulation at the region. To demonstrate the enhancement of phototransistor performance using LBGS, we previously studied amorphous silicon TFT with LBGS. The photosensitivity enhancement up to 64 times as compared to the conventional a-Si TFT. And then, we used multilayer MoS2 as a channel material to constitute phototransistor with LBGS. The giant photoreponsivity of 342.6 AW-1 at 2 mW cm-1 shows three orders of magnitude larger than that of global gate multilayer MoS2 TFTs. This result of our experiment and simulation reveals that multilayer MoS2 phototransistor with local bottom gate can be used as various photosensing devices.
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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".