Population congestion in 3-state quantum-dot cellular automata
Bibliographic record
Abstract
The behavior of quantum-dot cellular automata (QCA) networks is typically understood through considering polarization-like interactions with energies arising from the agreement or disagreement of the defined polarization states of neighboring QCA devices. It is known that additional interactions are present in 3-state molecular QCA that alter the required clocking fields needed for a device operation. Recent efforts in implementing logic gates using patterned dangling bonds (SiDBs) on hydrogen passivated silicon reveal significant challenges arising from similar effects. The necessary applied electrical potential needed to increase the population of an SiDB is strongly dependent on the current population of its neighbors, an effect we term congestion. It is unclear whether the strength of these interactions may pose an obstacle for future applications of SiDBs as a nanoscale QCA architecture. In this work, we investigate 3-state QCA in the regime in which congestion is significant and determine the extent to which such effects can be mitigated for SiDB devices. We propose that while SiDB-based QCA wires may be achievable depending on limitations of inter-dot tunneling, higher density devices such as majority gates may need to be replaced by more architecture specific implementations unless net-neutral variants of SiDB QCA devices can be demonstrated.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".