Soft error injection using advanced switch-level models for combinational logic in nanometer technologies
Bibliographic record
Abstract
Due to technology scaling, modern digital systems are becoming more prone to single-event transients (SETs) caused by radiation strikes in CMOS logic devices. This has led to the need for better soft error detection methods in order to increase the reliability of logic circuits in nanometer technologies. Present day soft error detection techniques assume that soft errors occur due to voltage pulses which change the logic state of a transistor node. A novel soft error detection concept is used, assuming that voltage fluctuations smaller than logic threshold can eventually result in soft errors. Advanced switch-level models were designed which mimic important characteristics of transistor-level circuits like bidirectional signal flow, driving strength variations and node capacitances and use verilog driving strengths to model different voltage values. The resulting switch-level models eliminate the complexity associated with state-of-art transistor level simulators while achieving desired amount of accuracy and faster simulation. The aim of this paper is to interpret various parameters used in these strength-based switch models in order to find an efficient way of injecting transients into complex logic circuits. The approach has been evaluated experimentally by creating a simulation environment which allows transient injection at internal nodes of switch-level circuits and injecting a wide range of input test vectors to ISCAS'85 benchmarks. The simulation results show that transient injection at drains of switch-level circuits gives better results in terms of accuracy and prevents over-estimation of soft error rate calculations as compared to injection at gates of transistors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".