A New SEC-DED Error Correction Code Subclass for Adjacent MBU Tolerance in Embedded Memory
Bibliographic record
Abstract
The reliability concern associated with radiation-induced soft errors in embedded memories increases as semiconductor technology scales deep into the sub-40-nm regime. As the memory bit-cell area is reduced, single event upsets (SEUs) that would have once corrupted only a single bit-cell are now capable of upsetting multiple adjacent memory bit-cells per particle strike. While these error types are beyond the error handling capabilities of the commonly used single error correction double error detection (SEC-DED) error correction codes (ECCs) in embedded memories, the overhead associated with moving to more sophisticated double error correction (DEC) codes is considered to be too costly. To address this, designers have begun leveraging selective bit placement to design SEC-DED codes capable of double adjacent error correction (DAEC) or triple adjacent error detection (TAED). These codes can be implemented for the same check-bit overhead as the conventional SEC-DED codes; however, no codes have been developed that use both DAEC and TAED together. In this paper, a new ECC scheme is introduced that provides not only the basic SEC-DED coverage but also both DAEC and scalable adjacent error detection ($x$AED) with a reduction in miscorrection probability as well. Codes capable of up to 11-bit AED have been developed for both 16- and 32-bit standard memory word sizes, and a (39, 32) SEC-DED-DAEC-TAED code implementation that uses the same number of check-bits as a conventional 32-data-bit SEC-DED code is presented.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".