Design and validation of a novel reconfigurable and defect tolerant JTAG scan chain
Bibliographic record
Abstract
In this paper, a novel technique to get a defect tolerant JTAG compliant scan chain in very large area integrated circuits (VLAIC) is presented. It was ruled that wafer-scale VLAICs require structural regularity and defect-tolerance to be cost effective. Using only one scan chain, as typically used in PCBs, would make the whole VLAIC unusable if a single defect is present in the chain. The proposed technique regularly distributes JTAG Test Access Port (TAP) controllers with test data ports linked to two or more neighbor test data ports. One TAP controller is wired as the entry point and another as the exit point of the scan chain that must be configured according to defect locations. An externally controlled wormhole like routing algorithm can be used for functional link discovery. This paper also proposes a mechanism to make defect tolerant access to test data registers, controlled from neighbor TAP controllers. Our technique has been successfully implemented and validated in a wafer-scale like integrated circuit used in a platform for electronic system prototyping. The logic area of this defect-tolerant configurable JTAG scan chain technique occupies 5% of the test logic and 0.3 % of the cell logic when links to four nearest neighbors are included.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".