Practical Dynamic Laser Stimulation Techniques for Complex Analog and Mixed Signal IC Failure Analysis
Bibliographic record
Abstract
Abstract The increasing electrical design and physical complexity of semiconductor devices, especially in the analog and mixed signal (AMS) applications, directly influences the development and evolution of fault isolation techniques. One of these techniques is Dynamic Laser Stimulation (DLS) which is widely used in the industry for effective identification of subtle failure mechanisms and soft defects especially for AC signal-related failures [1, 2]. However, for analysis of some complex AMS IC failure modes, the tool’s standard setup may not always be compatible with the biasing requirements of the device. For example, the setup would typically require expensive and intricate test systems (i.e. Automatic test equipment (ATE), SCAN tester, etc.) to be interfaced with the DLS tool for the analysis to be feasible and successful [3, 4]. This paper presents simple and practical techniques to implement DLS without the need for an expensive test support system. These techniques were applied in three different FA cases involving AMS ICs with complex and temperature-dependent failure modes. The results of subsequent analysis indicated success in isolating the exact defect sites.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".