Design-for-diagnosis: Your safety net in catching design errors in known good dies in CoWoS<sup>TM</sup>/3D ICs
Bibliographic record
Abstract
To meet power, performance and area requirements of modern electronic products, heterogeneous system integration where dies implemented in dedicated, optimized process technologies are stacked together to form a system is inevitable. The use of known-good pre-fabricated dies provides substantial reduction in time-to-market for integrated products. However, as dies from different suppliers using different technologies are used, finding source of design errors or manufacturing defects becomes very challenging if an integrated system fails in production. The system integrator has the onus to include test and diagnosis features that can enable post-silicon debugging. In this paper, we present a silicon diagnosis case study for a TSMC CoWoSTMbased heterogeneous 3D chip. We demonstrate how the Design-for-Diagnosis features implemented on the logic die were used to isolate interconnects testing failures. We were not only able to speed up the diagnosis but also able to find the real source of failure, which was a design and modeling issue in one of the 3rdparty known-good-die.
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.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".