MétaCan
Menu
Back to cohort
Record W2012233349 · doi:10.5539/mas.v9n1p34

Low Power Operational Amplifier in 0.13um Technology

2014· article· en· W2012233349 on OpenAlexvenueno aff
Muhammad Idzdihar Idris, Norbayah Yusop, Siti Amaniah Mohd Chachuli, Mohd Muzafar Ismail, Faiz Arith, A. M. Darsono

Bibliographic record

VenueModern Applied Science · 2014
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsnot available
FundersUniversiti Teknikal Malaysia MelakaIndian Council of Medical Research
KeywordsOperational transconductance amplifierDirect-coupled amplifierOperational amplifierElectrical engineeringCurrent-feedback operational amplifierGain–bandwidth productAmplifierComputer scienceFully differential amplifierPower gainOpen-loop gainElectronic engineeringCMOSEngineering

Abstract

fetched live from OpenAlex

Low power is one of the most indispensable criteria in several of application. In this paper a low power operational amplifier consists of two stages and operates at 1.8V power. It is designed to meet a set of provided specification such as high gain and low power consumption. Designers are able to work at low input bias current and also at low voltage due to the unique behavior of the MOS transistors in sub-threshold region. This two-stage op-amp is designed using the Silterra 130nm technology library. The layout has been draw and its area had been calculated. The proposed two stage op-amp consists of NMOS current mirror as bias circuit, differential amplifier as the first stage and common source amplifier as the second stage. The first stage of an op-amp contributed high gain while the second stage contributes a moderate gain. The results show that the circuit is able to work at 1.8V power supply voltage (VDD) and provides gain of 69.73dB and 28.406MHz of gain bandwidth product for a load of 2pF capacitor. Therefore, the power dissipation and the consistency of this operational amplifier are better than previously reported operational amplifier.

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 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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.943
Threshold uncertainty score0.409

Codex and Gemma teacher scores by category

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

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 head0.005
GPT teacher head0.189
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueModern Applied ScienceSame topicAnalog and Mixed-Signal Circuit DesignFrench-language works237,207