Effect of Large-Area Exfoliation and Etching on the Electrical Behavior of Transition-Metal Dichalcogenide Field-Effect Transistors
Bibliographic record
Abstract
Large-area layer transfer was used to fabricate multilayer transition-metal dichalcogenide (TMDC) thin-film transistor (TFT) arrays with areas of (40 × 40 μm 2 ) from single-crystal flakes. The effects of plasma etching of the TFT backchannel surface and bulk defects in the layers on the electrical performance and stability of the n-channel depletion-mode TFTs were investigated. Few-layer structures (∼3 monolayers) were fabricated using a dry etching process to thin multilayer (∼60–90 nm thick) TMDC structures. The etching improved the threshold voltage of the TFTs, resulting in a positive threshold voltage shift of +40 V after etching the backchannel, correlating to a bulk trap density of approximately 1 × 10 16 cm –3 eV –1 per monolayer. Etching the MoS 2 surface resulted in a threshold voltage shift of 0.2 V per nanometer of MoS 2 removed (for MoS 2 thicknesses >15 nm). For MoS 2 layers etched to <15 nm, the threshold voltage changed to ∼1.8 V per nanometer. An observed degradation of the carrier transport and electrical stability of these samples were found to be due to the proximity of the etched surface approaching the active channel region of the device. The results reveal the performance trade-offs of fabricating large-area arrays of few-layer TMDC TFTs using a mechanical exfoliation and dry etching approach.
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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 | 0.001 |
| 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".