A Transistor-Level Stochastic Approach for Evaluating the Reliability of Digital Nanometric CMOS Circuits
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Bibliographic record
Abstract
Over the last few decades, most quantitative measures of VLSI performance have improved by many orders of magnitude, this has been achieved by the unabated scaling of the sizes of MOSFETs. However, scaling also exacerbates noise and reliability issues, thus posing new challenges in circuit design. Reliability becomes a major concern due to many and often correlated factors, such as parameter variations and soft errors. Existing reliability evaluation tools focus on algorithmic development at the logic level that usually uses a constant error rate for gate failure and thus leads to approximations in the assessment of a VLSI circuit. This paper proposes a more accurate and scalable approach that utilizes a transistor-level stochastic analysis for digital fault modeling. It accounts for very detailed measures, including the probability of failure of individual transistors, the topology of logic gates, timing sequences and the applied input vectors. Simulation results are provided to demonstrate both the efficiency and the accuracy of the proposed approach.
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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 | 0.001 | 0.001 |
| 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 it