Low-Temperature Materials and Thin Film Transistors for Flexible Electronics
Bibliographic record
Abstract
This paper addresses the low-temperature deposition processes and electronic properties of silicon based thin film semiconductors and dielectrics to enable the fabrication of mechanically flexible electronic devices on plastic substrates. Device quality amorphous hydrogenated silicon (a-Si:H), nanocrystalline silicon (nc-Si), and amorphous silicon nitride (a-SiN/sub x/) films and thin film transistors (TFTs) were made using existing industrial plasma deposition equipment at the process temperatures as low as 75/spl deg/C and 120/spl deg/C. The a-Si:H TFTs fabricated at 120/spl deg/C demonstrate performance similar to their high-temperature counterparts, including the field effect mobility (/spl mu//sub FE/) of 0.8 cm/sup 2/V/sup -1/s/sup -1/, the threshold voltage (V/sub T/) of 4.5 V, and the subthreshold slope of 0.5 V/dec, and can be used in active matrix (AM) displays including organic light emitting diode (OLED) displays. The a-Si:H TFTs fabricated at 75/spl deg/C exhibit /spl mu//sub FE/ of 0.6 cm/sup 2/V/sup -1/s/sup -1/, and V/sub T/ of 4 V. It is shown that further improvement in TFT performance can be achieved by using n/sup +/ nc-Si contact layers and plasma treatments of the interface between the gate dielectric and the channel layer. The results demonstrate that with appropriate process optimization, the large area thin film Si technology suits well the fabrication of electronic devices on low-cost plastic substrates.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".