Unifying Figures of Merit: A Versatile Cost Function for Silicon Dangling Bond Logic
Bibliographic record
Abstract
As Silicon Dangling Bond (SiDB) logic emerges as a promising beyond-CMOS technology, Figures of Merit (FoMs) to assess gate performance become crucial in implementing devices that are robust against environmental variations. Con-structing robust SiDB logic involves designing gates that excel across multiple FoMs. However, there exist no clear guidelines on the ideal ranges for FoM values, nor a systematic approach to designing SiDB gates that optimize across multiple FoMs. Motivated by this, this work focuses on addressing the following key objectives: 1) Introduction of a new FoM, called Band Bending Resilience. 2) Determination, presentation, and detailed discussion on the best achievable values for each FoM for all 2-input Boolean functions. 3) Presentation of the versatile cost function χ, unifying multiple FoM$s$tailored to specific application requirements and priorities. 4) Implementation of the optimization strategy using the cost function χ, which aims at designing SiDB logic with minimal cost, ensuring an optimal balance between all FoMs. Overall, this research contributes significantly to the understanding of SiDB logic, establishing a basis for future progress in the field.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".