Non-linear test pattern generators for built-in self-test
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Various linear finite state machines have been widely used as pseudo-random test pattern generators for the built-in self-test of integrated circuits. These generators are inexpensive to use as they have the advantage of low hardware overhead. However, the Geffe generator, a classic type of non-linear finite state machine, has not been frequently studied or used in similar applications. It is known that a Geffe generator, when used as a pattern generator for digital system testing gives improved fault detection. Unfortunately, the area overhead involved is sufficiently high, thus, such a generator becomes impractical for built-in self-test. To solve such problems, we introduce two possible new designs of the Geffe generator. These new designs are based on the generator's original architecture, so they preserve the non-linear structure. Our optimal goal is to achieve a very sharp reduction of the area overhead, and maintain a satisfactory fault detection capability. These two new designs are exercised in the fault simulations of benchmark sequential circuits. The experimental results demonstrate both of our designs can lead to fault coverage, which significantly exceeds the fault coverage of linear machines, and is comparable to the original Geffe generator.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 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