DfX - Design for Excellence - How to Build a Consistent Design-to-Test Flow in Order to Deliver Defect-Free PCBAs
Bibliographic record
Abstract
ABSTRACT This paper is in English Only. Design for Excellence (DfX) can be used as part of an organization's Continuous Improvement Programme to decrease product development time, product cost and manufacturing cycle time, while increasing product quality, reliability and ultimately the customer satisfaction. It will significantly decrease the overall cycle time from the design concept to customer delivery, which is a critical success factor. Design for Excellence makes it possible to implement a Lean Test approach that produces a lower cost product whilst maintaining the highest quality. ASTER's vision is articulated on two principles: 1.Using traceability and repair loop information in order to qualify the customer defect universe. The defects include design defects, manufacturing defects and functional defects. 2.Using TestWay to import the defect opportunities and identify the possible consequences of inadequate testability and test coverage on a new design.
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How this classification was reachedexpand
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 teacher head, 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".