Bibliographic record
Abstract
Summary form only given, as follows. The problem of designing efficient and effective tests for semiconductor memories poses a daunting challenge to the test engineer. As commodity memory capacities approach the 1 Gb level by the end of this decade, testing cost becomes the largest component of the total cost of production. It is therefore essential to understand the precise nature of memory defects and failure mechanisms and to therefore be in the best position to design the most economic tests. A further complication in recent years has been the proliferation of specialized memory technologies, configurations and data access modes. This tutorial presentation focuses on reviewing the important fundamental concepts and techniques that are required to design high-quality tests for testing the cell arrays of dynamic random-access memories. Much of the memory testing literature has considered rather abstract functional fault models that appear to have little obvious justification in terms of observed faulty behaviors. In particular, much of the literature has dealt with fault models that would seem more appropriate for testing static rather than dynamic memory. The much larger production volume of DRAMs compared to that of SRAMs justifies specialized DRAM test methods. The topics covered in this presentation include the following: a brief review of DRAM architecture and operation; DRAM-specific defects and failure mechanisms; sources of soft failures and array noise; DRAM-specific fault models; the design of tests for the cell array; tests for fault location and diagnosis; and recommended tests for embedded DRAMs.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".