Defect-tolerant fpga switch block and connection block with fine-grain redundancy for yield enhancement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it