Impact of Process Imperfection of CNFET on Circuit-level Performance and Proposal to Improve Using Approximate Circuits
Bibliographic record
Abstract
Despite tremendous potential shown by emerging low dimensional materials including carbon nanotube field effect transistor (CNFET) technology for sub-10 nm transistors, the current processes for their fabrication still lack precise control and material quality; resulting in transistors suffering from process imperfections. With likely degradation in circuit performance due to poor process quality, the current CNFET technology will be more suitable for error resilient applications involving approximate circuits. A methodology is first provided considering the effect of imperfect CNFET process on circuit performance. Secondly, a systematic methodology is provided for generating approximate circuits to reduce the effect of process induced degradation due to imperfect process. With example of 16-bit CNFET adder, the two approximate adders at PCNT <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">open</sub> = 5%, achieved circuit-level pass rate of 62.7% and 71.8% respectively in comparison to only 8.4% for the precise adder; with relative logic error of 3.3% and 24.0% respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".