Designing majority gate-based nanoscale two-dimensional two-dot one-electron parity generator and checker for nano-communication
Bibliographic record
Abstract
At the present time, logic circuits design prototypes with quantum-dot cellular automata (QCA) have been comprehensively researched. The confines of orthodox CMOS technology induce to the breakthroughs of different technologies, one of which is QCA. Thoughtlessly, because of the deficiency of advance assembly support, QCA circuits frequently agonize from several sorts of manufacture shortcomings and variations and, hence, are error prone and defective. QCA technology is forming its aspect due to extreme effectiveness and rapidity with lesser area requirement. This study, a novel architecture of parity generator and checker, is proposed based on two-dot one-electron cells. Parity generator and checker assist in impeccable binary information communication from point to point. With the outlined parity generator and checker circuit, a nano-communication architecture is designed. The designed architecture is rationalized with a competently established standard mathematical operation based on Coulomb’s theory. All the outlined design contains a minimum number of cells, extent, and energy compared to existing four-dot two-electron QCA designs. The outlined designs comprehend minimum majority gate and latency. Besides, power depletion by the designs is measured and it is perceived that the total energy and power required to operate these designs are incredibly low.
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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) | 0.001 | 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".