Analysis of Error Masking and Restoring Properties of Sequential Circuits
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Bibliographic record
Abstract
Scaling of CMOS technology into nanometric feature sizes has raised concerns for the reliable operation of logic circuits, such as in the presence of soft errors. This paper deals with the analysis of the operation of sequential circuits. As the feedback signals in a sequential circuit can be logically masked by specific combinations of primary inputs, the cumulative effects of soft errors can be eliminated. This phenomenon, referred to as error masking, is related to the presence of so-called restoring inputs and/or the consecutive presence of specific inputs in multiple clock cycles (equivalent to a synchronizing sequence in switching theory). In this paper, error masking is extensively analyzed using the operations of state transition matrices (STMs) and binary decision diagrams (BDDs) of a finite state machine (FSM) model. The characteristics of state transitions with respect to correlations between the restoring inputs and time sequence are mathematically established using STMs; although the applicability of the STM analysis is restricted due to its complexity, the BDD approach is more efficient and scalable for use in the analysis of large circuits. These results are supported by simulations of benchmark circuits and may provide a basis for further devising efficient and robust implementations when designing FSMs.
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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 | 0.000 | 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 it