On-the-fly Defect-Aware Design of Circuits based on Silicon Dangling Bond Logic
Bibliographic record
Abstract
Silicon Dangling Bonds (SiDBs) have emerged as a promising post-CMOS technology for achieving ultra-low power dissipation, establishing themselves as a highly anticipated and environmentally friendly competitor in the realm beyond conventional CMOS. To support the SiDB logic framework, design automation approaches have rapidly evolved. However, at the atomic scale of SiDBs, material imperfections pose a significant roadblock in scaling these devices. Consequently, es-tablished design automation flows, which are defect-agnostic, are inadequate and have not kept pace with the latest experimental findings and advances in fabrication capabilities. A first attempt was recently proposed that extends established defect-agnostic physical design methods by rudimentary defect-aware capabilities. While promising at first glance, in this work, we show that this first attempt yields unsatisfactory results. Subsequently, we present a novel approach that automatically designs a tailored SiDB gate on-the-fly whenever an SiDB gate encounters atomic defects in its vicinity, thereby incorporating these atomic defects into its layout as an integral part. Our experimental evaluations confirm that the proposed approach is capable of designing SiDB circuits of significant complexity and size, even in the presence of atomic defects for the first time. Therefore, this work contributes to advancing this promising post-CMOS technology.
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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) | 0.001 | 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".