Unlocking Flexible Silicon Dangling Bond Logic Designs on Alternative Silicon Orientations
Bibliographic record
Abstract
With the impending plateau of Moore's Law, the search for novel computational paradigms has intensified. Silicon dangling bond (SiDB) logic emerges as a promising avenue in this quest, leveraging the quantum-dot-like properties of SiDBs and atomically precise fabrication techniques to realize logic functions at the nanometer scale. Advances in computer-aided design (CAD) tools specialized for SiDB logic exploration have also opened the door to novel logic research from the gate- to application-level. This paper introduces a lattice vector formulation for SiDB logic designs on alternative silicon lattice orientations, enabling the exploration of logic gates on arbitrary lattice orientations and addressing the limitations of previous SiDB logic research confined to the H-Si(100)-2 ×1 surface. A comprehensive workflow for designing standard tile libraries compatible with design automation frameworks is proposed, facilitating the scaling of SiDB layouts to large-scale systems implementation on multiple lattice orientations. We demonstrate the proposed lattice vector representation and the library design workflow through a case study on the H-Si(111)-1×1 surface, showcasing the first logic gates designed for this orientation. This advancement opens new avenues for SiDB logic research, enabling rigorous evaluations of various lattice orientations for future logic design studies and experimental investigations.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".