Aliasing-Free Space Compaction in VLSI with Cascade of Two-Input OR/NOR Logic
Bibliographic record
Abstract
Designing aliasing-free space support hardware for built-in self-testing in very large scale integration circuits and systems is of immense significance, specifically due to the design paradigm shift in recent years from system-on-board to system-on-chip. This paper develops an approach to designing aliasing-free space compaction hardware targeting particularly embedded cores-based system-on-chips for single stuck-line faults, extending well known concept from conventional switching theory, viz. that of compatibility relation as used in the minimization of incompletely specified sequential machines. For a pair of response outputs of the circuit under test, the method introduces the notion of fault detection compatibility and conditional fault detection compatibility (conditional upon some other response output pair being simultaneously fault detection compatible) with respect to two-input OR/NOR logic. The process is illustrated with design details of space compactors for the International Symposium on Circuits and Systems or ISCAS 85 combinational (and ISCAS 89 full-scan sequential) benchmark circuits using simulation programs ATALANTA and FSIM, attesting to the importance of the technique from the viewpoint simplicity, resultant low area overhead and full fault coverage for single stuck-line faults, thereby making it an appropriate choice in commercial design environments.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".