Design of multiplexer in amorphous silicon technology
Bibliographic record
Abstract
The large area capability of amorphous silicon technology finds many active matrix displays and imaging arrays employing the a-Si:H thin film transistors (TFTs) as analog switches. The TFTs are limited to simple switching applications due to the low driving current and metastability issues inherent to a-Si:H technology. The most apparent metastability issue in an a-Si:H TFT is threshold voltage (Vt) shift due to prolonged gate bias. The Vt shift is more pronounced for positive gate biases than for negative gate biases. Moreover, in the presence of a pulse gate bias, the Vt shift depends on the frequency of operation. Considering the large number of gate and data lines in displays and imaging arrays, multiplexer circuits can provide benefits such as reducing the pin count of the array, reducing the number of external chips and hence, overall system cost. In amorphous silicon technology, Pass transistor logic (PTL)-based multiplexers (MUXes) are attractive since they can be designed to have manageable Vt shifts and less transistors than logic gates based MUXes. In this article, we present Vt shift measurements on in-house fabricated a-Si:H TFTs under relevant bias conditions and analyze the performance of a PTL-based MUX circuit in a-Si:H technology.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".