(Digital Presentation) Multimodal Encapsulation of p-SnOx to Engineer the Carrier Density for Thin Film Transistor Applications
Bibliographic record
Abstract
It has been challenging to synthesize p-type SnO x (1≤x<2) and engineer the electrical properties such as carrier density and mobility due to the narrow processing window and the localized oxygen 2p orbitals near the valence band. We recently reported on the processing of p-type SnOx and an oxide-based p-n heterostructures, demonstrating high on/off rectification ratio (>10 3 ), small turn-on voltage (<0.5 V), and low saturation current (~1×10 -10 A) 1 . In order to further understand the p-type oxide and engineer the properties for various electronic device applications, it is important to identify (or establish) the dominating doping and transport mechanisms. The low dopability in p-type SnOx, of which the causation is also closely related to the narrow processing window, needs to be mitigated so that the electrical properties of the material are to be adequately engineered 2, 3 . Herein, we report on the multifunctional encapsulation of p-SnO x to limit the surface adsorption of oxygen and selectively permeate hydrogen into the p-SnO x channel for thin film transistor (TFT) applications. Time-of-flight secondary ion mass spectrometry measurements identified that ultra-thin SiO 2 as a multifunctional encapsulation layer effectively suppressed the oxygen adsorption on the back channel surface of p-SnO x and augmented hydrogen density across the entire thickness of the channel. Encapsulated p-SnO x -based TFTs demonstrated much-enhanced channel conductance modulation in response to the gate bias applied, featuring higher on-state current and lower off-state current. The relevance between the TFT performance and the effects of oxygen suppression and hydrogen permeation is discussed in regard to the intrinsic and extrinsic doping mechanisms. These results are supported by density-functional-theory calculations. Acknowledgement This work was supported by the U.S. National Science Foundation (NSF) Award No. ECCS-1931088. S.L. and H.W.S. acknowledge the support from the Improvement of Measurement Standards and Technology for Mechanical Metrology (Grant No. 20011028) by KRISS. K.N. was supported by Basic Science Research Program (NRF-2021R11A1A01051246) through the NRF Korea funded by the Ministry of Education. References Lee, D. H.; Park, H.; Clevenger, M.; Kim, H.; Kim, C. S.; Liu, M.; Kim, G.; Song, H. W.; No, K.; Kim, S. Y.; Ko, D.-K.; Lucietto, A.; Park, H.; Lee, S., High-Performance Oxide-Based p–n Heterojunctions Integrating p-SnOx and n-InGaZnO. ACS Applied Materials & Interfaces 2021, 13 (46), 55676-55686. Hautier, G.; Miglio, A.; Ceder, G.; Rignanese, G.-M.; Gonze, X., Identification and design principles of low hole effective mass p-type transparent conducting oxides. Nat Commun 2013, 4 . Yim, K.; Youn, Y.; Lee, M.; Yoo, D.; Lee, J.; Cho, S. H.; Han, S., Computational discovery of p-type transparent oxide semiconductors using hydrogen descriptor. npj Computational Materials 2018, 4 (1), 17. Figure 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".