Fault tolerance in systems design in VLSI using data compression under constraints of failure probabilities
Bibliographic record
Abstract
The design of space-efficient support hardware for built-in self-testing (BIST) is of critical importance in the design and manufacture of VLSI circuits. This paper reports new space compression techniques which facilitate designing such circuits using compact test sets, with the primary objective of minimizing the storage requirements for the circuit under test (CUT) while maintaining the fault coverage information. The compaction techniques utilize the concepts of Hamming distance, sequence weights, and derived sequences in conjunction with the probabilities of error occurrence in the selection of specific gates for merger of a pair of output bit streams from the CUT. The outputs of the space compactor may eventually be fed into a time compactor (viz. syndrome counter) to derive the CUT signatures. The proposed techniques guarantee simple design with a very high fault coverage for single stuck-line faults, with low CPU simulation time, and acceptable area overhead. Design algorithms are proposed in the paper, and the simplicity and ease of their implementations are demonstrated with numerous examples. Specifically, extensive simulation runs on ISCAS 85 combinational benchmark circuits with FSIM, ATALANTA, and COMPACTEST programs confirm the usefulness of the suggested approaches.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".