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Record W1954777664 · doi:10.1109/irws.1999.830551

Signal margin test to identify process sensitivities relevant to DRAM reliability and functionality at low temperatures

2003· article· en· W1954777664 on OpenAlex
Erik Nelson, Y. Li, D. Poindexter, M. Ruprecht, Eui-Taek Lim, Y. Matsubara, Hiroshi Sawazaki, Q. Ye, M. Iwatake, W. Tonti

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIntegrated Circuits and Semiconductor Failure Analysis
Canadian institutionsInfineon Technologies (Canada)
Fundersnot available
KeywordsDramReliability (semiconductor)Margin (machine learning)Computer scienceReliability engineeringSIGNAL (programming language)Electronic engineeringProcess (computing)AmplifierCircuit reliabilityEngineeringComputer hardwareCMOSPower (physics)Physics

Abstract

fetched live from OpenAlex

With high aspect ratio, tight spacing, small line widths, and low supply voltages associated with the scaling of the DRAM cell, signal for the sense amplifier becomes weaker for each new DRAM generation. We have developed a signal margin testing methodology capable of identifying process sensitivities relevant to DRAM functionality and reliability at low temperatures. This paper describes the test methodology and discusses the benefits derived from applying this method to 256M DRAM product development.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.769

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.007
GPT teacher head0.230
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it