Design and Implementation of New Coplanar FA Circuits without NOT Gate and Based on Quantum-Dot Cellular Automata Technology
Bibliographic record
Abstract
The miniaturization of electronic devices and the inefficiency of CMOS technology due to the development of integrated circuits and its lack of responsiveness at the nanoscale have led to the acquisition of nanoscale technologies. Among these technologies, quantum-dot cellular automata (QCA) is considered one of the possible replacements for CMOS technology because of its extraordinary advantages, such as higher speed, smaller area, and ultra-low power consumption. In arithmetic and comparative circuits, XOR logic is widely used. The construction of arithmetic logic circuits using AND, OR, and NOT logic gates has a higher design complexity. However, XOR gate design has a lower design complexity. Hence, the efficient and optimized XOR logic gate is very important. In this article, we proposed a new XOR gate based on cell-level methodology, with the expected output achieved by the influence of the cells on each other; this design method caused less delay. However, this design was implemented without the use of inverter gates and crossovers, as well as rotating cells. Using the proposed XOR gate, two new full adder (FA) circuits were designed. The simulation results indicate the advantage of the proposed designs compared with previous structures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".