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Record W2028643888 · doi:10.1109/tcsii.2013.2268312

An Energy-Efficient Offset-Cancelling Sense Amplifier

2013· article· en· W2028643888 on OpenAlex

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

VenueIEEE Transactions on Circuits & Systems II Express Briefs · 2013
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsInput offset voltageOffset (computer science)Sense amplifierStatic random-access memoryAmplifierVoltageTransistorBiasingSense (electronics)Process variationElectrical engineeringElectronic engineeringEnergy consumptionComputer scienceMaterials scienceOperational amplifierOptoelectronicsEngineeringCMOS

Abstract

fetched live from OpenAlex

As SRAM cells are scaled aggressively to increase the overall packing density, the smaller transistors exhibit higher degrees of process variation and mismatch, leading to larger offset voltages. For SRAM sense amplifiers (SAs), higher offset voltages lead to an increased likelihood of an incorrect decision. In this brief, an SA capable of cancelling the input offset voltage is presented. The simulated and measured results in 180-nm technology show that the SA is capable of detecting a 4-mV differential input signal under dc and transient conditions. The proposed SA when compared with other offset cancellation schemes exhibits comparable offset cancellation performance with a smaller delay and significantly lower energy consumption.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.780
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.195
Teacher spread0.184 · 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