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Record W4296925072 · doi:10.2478/jee-2022-0036

Energy estimation of QCA circuits: An investigation with multiplexers

2022· article· en· W4296925072 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Electrical Engineering · 2022
Typearticle
Languageen
FieldComputer Science
TopicQuantum-Dot Cellular Automata
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMultiplexerCMOSQuantum dot cellular automatonEnergy (signal processing)Electronic circuitComputer scienceElectronic engineeringMultiplexingPhysicsElectrical engineeringEngineeringTelecommunicationsQuantum mechanics

Abstract

fetched live from OpenAlex

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..

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.705
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.190
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it