Defect-tolerant fpga switch block and connection block with fine-grain redundancy for yield enhancement
Bibliographic record
Abstract
Future process nodes have such small feature sizes that there will be an increase in the number of manufacturing defects per die. For large FPGAs, it will be critical to tolerate multiple defects (Campregher et al., 2005). We propose a number of changes to the detailed routing architecture of island-style FPGAs to tolerate multiple random, distributed interconnect defects without re-routing and with minimal impact on signal timing. Our scheme is a user option prebuilt into an architecture, requiring +11% area for additional multiplexers. Unused (spare) wiring tracks are also needed, bringing total overhead to 24% to tolerate stuck-at or open faults, or 34% to include bridging. User circuits that do not fully stress the routing network already have these tracks freely available. The delay penalty is programmable: 5-10% if defect rates are expected to be sufficiently low, but can be as high as 25% if defect rates are high. Our schemes can tolerate more than 10 interconnect defects for large array sizes of 128 /spl times/ 128. Unlike row/column redundancy schemes, our schemes are scalable: they naturally tolerate more defects as the FPGA array size increases. This work is the first detailed analysis of fine-grained defect-tolerant schemes in FPGAs.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".