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Record W3201577719 · doi:10.3390/electronics10182311

A High Bandwidth-Power Efficiency, Low THD2,3 Driver Amplifier with Dual-Loop Active Frequency Compensation for High-Speed Applications

2021· article· en· W3201577719 on OpenAlexaff
Ximing Fu, Kamal El‐Sankary, Yadong Yin

Bibliographic record

VenueElectronics · 2021
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsDalhousie University
Fundersnot available
KeywordsTotal harmonic distortionPhase marginAmplifierFrequency compensationSlew rateCapacitorElectrical engineeringEngineeringOperational amplifierElectronic engineeringControl theory (sociology)VoltageCMOSComputer science

Abstract

fetched live from OpenAlex

This paper presents a driver amplifier with high bandwidth-power efficiency, high capacitor-driving capacity, and low total harmonic distortion (THD). One complementary differential pair composed of self-cascode transistors is incorporated to obtain a full input voltage swing. Flipped voltage follower (FVF) buffers are applied as second stage to drive the last class-AB output stage. Moreover, a dual-loop active-feedback frequency compensation (DLAFC) is presented, which can stabilize the proposed multistage amplifier and keep the dominant pole on high frequency to obtain high-frequency total harmonic distortion (THD) suppression. To achieve a low-frequency phase margin protection (PMP), one left half-plane (LHP) zero is introduced to compensate for the nondominant pole caused by the load capacitor. Meanwhile, two high-frequency LHP zeros are injected to achieve high-frequency phase margin boosting (PMB) and reduce the amplifier’s settling time and integration area. This proposed amplifier is implemented in a standard DBH 0.18 μm 5 V CMOS process, and it achieves over 115-dB DC gain, 150–300 MHz GBW under 0–100 p load capacitors, ultra-high THD2,3 suppression ranges from 100 kHz to 10 MHz under 1–2 V output swing, and over 250 V/μs average slew rate, by only dissipating 12.5 mW at 5 V power supply.

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 score0.880
Threshold uncertainty score0.903

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)0.0000.000

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 head0.006
GPT teacher head0.200
Teacher spread0.194 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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