An Energy-Aware Nanoscale Design of Reversible Atomic Silicon Based on Miller Algorithm
Bibliographic record
Abstract
Atomic silicon and reversible logic are domain field-coupled nanocomputing (FCN) techniques that have drawn significant attention for their lower power consumption, area, and design overhead. As atomic silicon and reversible logic reach dramatically reduced occupied area and power consumption, they can be a suitable alternative to CMOS technology. These technologies can significantly reduce the occupied area and energy consumption in all kinds of digital circuits, which are the two most challenging aspects of developing digital circuits. On the other hand, the Miller algorithm is a crucial synthesis for suggesting reversible circuits with extraordinary techniques in nanotechnology. It is an exceptionally effective and systematic method based on quantum rules for designing and proposing reversible circuits that can help suggest a reversible gate with low energy and a low occupied area. This study aims to construct novel nano-scale circuits with a focus on low-occupied area and minimal energy consumption as essential factors while designing digital circuits. In this paper, we propose a reversible gate with the well-known Miller algorithm and atomic silicon technology. Then it is used to develop a reversible full adder, 4-bit ripple carry adder, and 4:2 compressor. Finally, the proposed structures are simulated using the SiQAD tool.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".