Space compression revisited
Bibliographic record
Abstract
This paper discusses new space compression techniques for built-in self-testing (BIST) of VLSI circuits based on the use of compact test sets to minimize the storage requirements for the circuit under test (CUT) while maintaining the fault coverage information, utilizing the concepts of Hamming distance, sequence weights along with failure probabilities of errors in the selection of specific gates for merger of output streams from the CUT. The outputs coming out of the space compactor may eventually be fed into a time compressor to derive the signature for the circuit. The concepts are extended to establish generalized mergeability criteria for merging an arbitrary number N of output bit streams under conditions of both stochastic independence and dependence of line errors. The proposed techniques guarantee rather simple design with high fault coverage for single stuck-line faults, with low CPU simulation time, and acceptable area overhead. Design algorithms are also proposed, and the simplicity and ease of implementation are demonstrated with examples, primarily through extensive simulation runs on ISCAS 85 combinational benchmark circuits with FSIM, ATALANTA, and COMPACTEST. The paper also provides performance comparisons of the designed space compactors with conventional linear parity tree space compressor.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".