Addressing the Cost and Execution Challenges of Scan Chain Testing and Failure Analysis of Complex IC’s
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
Abstract Analyzing scan chain failures is challenging without dedicated test hardware. Traditional solutions like ATE testers and compact diagnosis tools have significant drawbacks: they're expensive, require complex hardware customization and proprietary software licenses, and need substantial lab space. This paper presents a cost-effective alternative: a portable, flexible, and fully customizable bench-top scan chain testing system that easily integrates with fault isolation tools. Using an off-the-shelf embedded development tool, we replicated the complete scan chain testing process—from pattern generation to test vector transmission/reception and results comparison. The system reduces costs by approximately 200-fold compared to traditional solutions. We validated our approach by analyzing a device with marginal and frequency-dependent stuck-at-scan failures. Using DALS (Dynamic Analog Laser Stimulation), we successfully localized the defect and confirmed it through mechanical delayering, FIB cross-sectioning, and SEM imaging.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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