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Fully Integrated Dual-Channel Gate Driver and Area Efficient PID Compensator for Surge Tolerant Power Sensor Interface

2020· article· en· W3047891514 on OpenAlexaff
Mostafa Amer, Mohamed R. Ali, Ahmed Abuelnasr, Ahmad Hassan, Morteza Nabavi, Yvon Savaria, Mohamad Sawan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsSettling timePID controllerOvershoot (microwave communication)CMOSGate driverBandwidth (computing)Electrical engineeringResistorVoltageComputer scienceTransient responseElectronic engineeringEngineeringStep responseTelecommunicationsTemperature control

Abstract

fetched live from OpenAlex

This paper presents a power sensor interface (PSI) driving a wide range of load valves with coil resistance ranging from 5 Ω to 62.5 Ω. It can sustain voltage surges up to 115 V. An integrated high-voltage (HV) dual-channel gate driver with 8.7 ns deadtime (DT) is implemented to efficiently drive e-GaN FETs in a synchronous DC-DC buck converter. In addition, an area-optimization technique of on-chip passive components is proposed to enable full-integration of voltage-mode controller with 87% reduction in area. The achieved peak efficiency is 98.7% @ 4.67 A load. The feedback bandwidth is ~100 kHz to maintain fast transient response at 1MHz switching frequency. The settling time is <; 70 μs and <; 100 μs at load changes of -5.2 A and 5.2 A respectively. Overshoot (OS) and undershoot (US) voltages are within 2% of nominal V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OUT</sub> . The layout of the gate driver and feedback control is implemented in 0.35-μm HV CMOS process with active die area of 0.75 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.612
Threshold uncertainty score0.956

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 head0.014
GPT teacher head0.207
Teacher spread0.193 · 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 designSimulation or modeling
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

Citations8
Published2020
Admission routes1
Has abstractyes

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