MétaCan
Menu
Back to cohort
Record W2584580894 · doi:10.1049/iet-cds.2016.0416

Positive feedback technique and split‐length transistors for DC‐gain enhancement of two‐stage op‐amps

2017· article· en· W2584580894 on OpenAlexaff
Seyed Mahmoud Anisheh, Hossein Shamsi, Mitra Mirhassani

Bibliographic record

VenueIET Circuits Devices & Systems · 2017
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsOpen-loop gainTotal harmonic distortionPhase marginTransistorLoop gainInput offset voltageBandwidth (computing)Gain compressionControl theory (sociology)Operational amplifierVoltageAmplifierElectronic engineeringPhysicsEngineeringComputer scienceCMOSElectrical engineeringTelecommunications

Abstract

fetched live from OpenAlex

This study presents the design and simulation of a fully differential two‐stage op‐amp in a 0.18 μm complementary metal–oxide–semiconductor process with a 1.8 V supply voltage. In this op‐amp, positive feedback technique and split‐length transistors (SLTs) are employed to increase the DC‐gain of the op‐amp by about 22 dB without affecting the unity‐gain bandwidth (UGBW), stability, power dissipation and output voltage swing of the conventional two‐stage op‐amp. A comprehensive analysis is provided for differential‐mode gain, common‐mode gain, power supply rejection ratio, input‐referred noise, input offset, frequency response and the effect of using SLTs on DC‐gain sensitivity. The proposed op‐amp is utilised in a flip‐around sample‐and‐hold amplifier (SHA). The output spectrum of the SHA shows the total harmonic distortion of 0.0023%. The post‐layout and Monte Carlo simulation results show that the proposed op‐amp has better performance than the state‐of‐the‐art designs.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 head0.021
GPT teacher head0.253
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), 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

Citations16
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueIET Circuits Devices & SystemsSame topicAnalog and Mixed-Signal Circuit DesignFrench-language works237,207