Methodology to Capture Statistical Effect of Process Imperfections on Glitch Suppression in CNFET Circuits and to Improve by Using Approximate Circuits
Bibliographic record
Abstract
Carbon nanotube field effect transistor (CNFET) technology has shown tremendous potential, and thus extensively studied among the emerging materials-based technologies to replace Si for the post-Si era. However, emerging technologies including CNFET technology suffer from immature, poor process quality, leading to process imperfections, which in turn degrade circuit performance. This paper presents a methodology to evaluate circuit level impact and design solution related with imperfect process. Monte Carlo simulation is applied to consider the statistical nature of the process imperfections. With a specific study on glitch tolerance (important circuit-level performance metric in advanced technology nodes) in CNFET circuits, our simulation framework provides the link between degradation in glitch tolerance with poor process quality. Moreover, we have proposed that approximate circuits can significantly improve glitch tolerance in comparison to precise counterparts, due to lesser nodes, reduced stacked configurations, and reduced number of connections at some nodes because of simpler topologies. With an example of 4-bit adder and 4-bit multiplier circuits, we have demonstrated that approximate circuits are able to lower the glitch vulnerability to an acceptable level, with a tolerable logic error. Moreover, approximate circuits would also relax the requirement on process quality to keep process-induced failures below certain target value.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".