Reliable SRAM using NAND‐NOR Gate in beyond‐CMOS QCA technology
Bibliographic record
Abstract
Abstract The rise in complementary metal‐oxide semiconductor (CMOS) limitations has urged the industry to shift its focus towards beyond‐CMOS technologies to stay in race with Moore’s law. Quantum‐dot cellular automata (QCA) is considered to be a prominent paradigm among the emerging beyond‐CMOS technologies. Since QCA is an emerging technology with no proper layout tools, layout generation from hardware description language (HDL) can be done by implementing circuits using the NAND‐NOR logic. In QCA, the NAND‐NOR logic is realised by combining a majority gate and an inverter or by using some dedicated structures. The Radius of Effect (RoE) is a critical factor that depends on the permittivity of the material used and it has an influence on the columbic interaction, polarisation and kink energy. Lower Radius of Effect values will have an impact on the performance of the circuit. In this work, a cost‐efficient NAND‐NOR gate using Single Rotated Cell (SRC) inverter is proposed which can operate with lower Radius of Effect. Using the proposed gate, multiplexer, decoder, and innovative memory cell are implemented. In order to demonstrate the ability to implement larger circuits using NAND‐NOR logic and the proposed blocks, a 16*16 SRAM is implemented. QCADesigner is used for the simulation and validation of the proposed designs.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".