A built-in self-testing method for embedded multiport memory arrays
Bibliographic record
Abstract
With recent advances in semiconductor technologies, the design and use of memories for realizing complex system-on-a-chip (SoC) is very widespread. The growing need for storage in computer, communication, and network appliances has motivated new advancements in faster and more efficient ways to test memories. Efficient testing schemes for single-port memories have been readily available. Multiport memories are widely used in multiprocessor systems, telecommunication application-specific integrated circuits (ASICs), etc. Research papers which define multiport memory fault models and give march tests for the same are currently available. However, little work has been done to use the power of serial interfacing for testing multiport memories. In this paper, we develop a powerful test architecture for two-port memories using the serial interfacing technique. Based on the serial testing mechanism, we propose new march algorithms which can prove effective to reduce hardware cost considerably for a chip with many two-port memories. Once we understand how serial interfacing helps test two-port memories, one possible extension is to use serial interfacing for p-port memories (p > 2). The proposed method based on the serial interfacing technique has the advantages of high fault coverage, low hardware overhead, and tolerable test application time.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".