A Versatile Strategy for Comprehensive Data Collection and Retention in Embedded SoC Memories
Bibliographic record
Abstract
In modern automotive system-on-chip (SoC) designs, large embedded flash memories have become a standard feature. Since they occupy a significant percentage of the die area, their impact on the SoCs’ overall yield is substantial, making them a critical component in the production process. Embedded memories are then deeply tested to unsure their reliability. The data collected through these tests are fundamental to chip designers and test engineers to iron out their designs and understand the most common failure mechanisms. A common approach for data collection is the generation of bitmaps based on the gathering of individual fail coordinates in a list-based fashion. Other more efficient compaction or compression approaches exist and all these approaches can use dedicated internal memories to store the result of a given test. Unfortunately, all the methods currently found in the literature do not allow diagnostic data retention along multiple tests, requiring constant and time-consuming communications with the external tester, increasing the test cost for the manufacturers. This article presents an on-chip algorithm to compact and retain diagnostic information from multi-step embedded memories testing. The foundation of this work lies in an efficient shape recognition and encoding algorithm. The collected information is stored in a dedicated nonvolatile on-chip memory. Information about the tests that generated a given set of fault shapes is also encoded in this dedicated diagnostic memory, enabling manufacturers to collect all the diagnostic information at the end of their test flow. Experimental results on over 110 Automotive SoCs made by Infineon TM show that using the proposed approach, 100% of the diagnostic information of devices undergoing a standard automotive-grade test flow is permanently encodable in a limited 24 KB diagnostic space while also consistently reducing the total test time of up to 53.8% with respect to traditional list-based approaches.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".