Reliable Synchronous and Asynchronous Counter Design in QCA
Bibliographic record
Abstract
Due to rapid growth in the integrated circuit (IC) industry, the demand for compact digital system design is high. However, the continued technology reductions made the feasibility of further scaling down transistor size more challenging. In response to the growing demand for ultra-compact IC designs, the revolutionary quantum-dot cellular automata (QCA) technology has emerged as a promising solution. In a digital era, the counters are widely adopted in the peer-to-peer process flow to establish a mechanism for generating unique values for each identifier/number. In this work, a unique synchronous and asynchronous counters architecture is proposed with a reliable D and T flip-flop design. The proposed QCA architecture is implemented and validated with the QCA designer tool. Furthermore, in QCA technology, unreliable QCA designs can lead to frequent errors and malfunctions in the implemented logic. To overcome this challenge, the proposed design prioritizes cell placement (the relative positions of QCA cells) to make the circuit more robust. As a result, the circuit can still produce the expected functionality even if some QCA cells malfunction. Hence, to ensure the reliability of the proposed QCA architecture, the missing cell defect analysis is carried out in comparison with existing state-of-the-art designs. Based on comparison results, the unique designs like the proposed multiplexer, D flip-flop and T flip-flop design exhibit success rates of 67.28, 77.04 and 85.15%, respectively. The experimental results demonstrate that the proposed counter-architecture outperforms existing architectures.
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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 | 0.001 |
| 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".