Defect Level Estimation for Pseudorandom Testing Using Stochastic Analysis
Why this work is in the frame
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Bibliographic record
Abstract
Pseudorandom testing has been widely used in built‐in self‐testing of VLSI circuits. Although the defect level estimation for pseudorandom testing has been performed using sequential statical analysis, no closed form can be accomplished as complex combinatorial enumerations are involved. In this work, a Markov model is employed to describe the pseudorandom test behaviors. For the first time, a closed form of the defect level equation is derived by solving the differential equation extracted from the Markov model. The defect level equation clearly describes the relationships among defect level, fabrication yield, the number of all input combinations, circuit detectability (in terms of the worst single stuck‐at fault), and pseudorandom test length. The Markov model is then extended to consider all single stuck‐at faults, instead of only the worst single stuck‐at fault. Results demonstrate that the defect level analysis for pseudorandom testing by only dealing with the worst single stuck‐at fault is not adequate (In fact, the worst single stuck‐at fault analysis is just a special case). A closed form of the defect level equation is successfully derived to incorporate all single stuck‐at faults into consideration. Although our discussions are primarily based on the single struck‐at fault model, it is not difficult to extend the results to other fault types.
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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 | 0.002 |
| 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