Diagnosis of intermittent faults and corresponding algorithm development beyond 5nm technologies
Bibliographic record
Abstract
Scaling down the transistor size enables a cost-effective high-performing solution in modern semiconductor designs. However, the added complexity of process and extremely small critical three-dimensional spacing of the transistor structure make it more challenging to maintain precise variation control. Defects introduced here can lead to different intermittent behaviors, some of which might not appear as fails during traditional testing procedures but might manifest as a system failure in the field. The emergence of artificial intelligence chip design has introduced high demands for a massive number of multi-cores connected in parallel with a huge memory array size in a chip. This further increases the importance of identifying rare tail events through in-depth diagnosis in advanced nodes. Various sophisticated algorithms have been proposed to improve defect coverage. However, the addition of algorithms may increase test cost tremendously while providing limited benefits for specific fault types which may not happen. Therefore, selecting a smart combination of algorithms that provide enough coverage for the specific product application is essential for cost-effective testing. In this paper, we review the electrical properties of marginal defects inserted in an SRAM device to evaluate test escapes in the latest technology. We also present a new algorithm to improve test coverage efficiency. A commercially available memory BIST tool was used to load a DFT compatible algorithm and operation set for memory test. A commercially available analog simulation tool was used in combination with a novel defect simulation flow to evaluate digital and analog behavior of the device.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".