Space Compactor Design in VLSI Circuits Based on Graph Theoretic Concepts
Bibliographic record
Abstract
The realization of space-efficient support hardware for built-in self-testing (BIST) is of great significance in VLSI circuits design. New approaches to designing aliasing-free space compaction hardware are proposed in the subject paper for testing cores-based system-on-chip (SOC) for single stuck-line faults, extending the well-known concepts of conventional switching theory, viz. those of cover table, frequency ordering commonly utilized in the simplification of switching functions, and of incompatibility relation to generate maximal compatibility classes using graph theoretic concepts, based on optimal generalized sequence mergeability, as developed by the authors in earlier works. The paper provides briefly the mathematical basis of selection criteria for merger of an optimal number of outputs of the circuit under test (CUT) to achieve maximum compaction ratio in the design, along with some partial simulation results on ISCAS 85 combinational benchmark circuits with programs ATALANTA and FSIM. The advantages of the suggested approaches are evident in achieving zero aliasing without any CUT modifications, while keeping the area overhead and signal propagation delay relatively low, besides their applicability with both deterministic compacted and pseudorandom test patterns
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".