Energy estimation of QCA circuits: An investigation with multiplexers
Bibliographic record
Abstract
Abstract Quantum-dot Cellular Automata (QCA) is a rival to complementary-metal-oxide-semiconductor (CMOS)-based technology and one of the most cutting-edge nano-scale technologies. The multiplexer is a fundamental component in the fields of nano communication and nano computation. The investigative item of this article is the QCA multiplexer, and a handful of the best multiplexers were chosen as samples for the current experiment. The QCA layouts were designed in the QCADesigner-2.0.3 simulation engine environment, and the best one was reported after successfully experimenting on a total of eight samples. The co-ordinate-based energy was estimated using QCADesigner-E (QDE), and the non-adiabatic energy waste was investigated using QCAPro. According to the coordinates-based technique, the overall energy waste of the best energy-saving QCA multiplexer is 5.90 meV, with an average energy loss per cycle of 0.537 meV. Another approach, QCAPro-based, was used to estimate the energy loss at three different levels of tunneling at a constant temperature, yielding an overall energy loss of approximately 12 to 15 meV for the energy-efficient multiplexers..
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".