MétaCan
Menu
Back to cohort
Record W2524945924 · doi:10.1109/tcsi.2016.2599180

Synthesis of High Gain Operational Transconductance Amplifiers for Closed-Loop Operation Using a Generalized Controller-Based Compensation Method

2016· article· en· W2524945924 on OpenAlexaff
Ming Yang, Gordon W. Roberts

Bibliographic record

VenueIEEE Transactions on Circuits and Systems I Regular Papers · 2016
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsMcGill University
Fundersnot available
KeywordsIntegratorTransconductanceElectronic engineeringCMOSFrequency compensationAmplifierComputer scienceRealization (probability)Open-loop gainLoop gainCompensation (psychology)Bandwidth (computing)Slew rateOperational amplifierOperational transconductance amplifierControl theory (sociology)EngineeringTransistorElectrical engineeringMathematicsTelecommunications

Abstract

fetched live from OpenAlex

This paper presents a systematic procedure that can be used to create operational transconductance amplifiers (OTAs) for closed-loop operation using multiple low-gain stages to realize extremely high DC gain. Such devices are necessary to realize analog functions with demanding absolute accuracy requirements, e.g., high-resolution ADCs and DACs. The principle is based on the cascade of undamped integrators to realize large DC gains and a state-space derived controller to stabilize its operation in a closed-loop configuration. A programmable OTA fabricated in the IBM 130 nm CMOS process is used as a test vehicle to prove the design principle through its 2 to 5th-order realization. Measured data reveals DC gains ranging from 50 to 150 dB with a 3-dB bandwidth of 10 kHz and a unity gain frequency of 10 MHz. While this paper demonstrates the design principles using CMOS integrated circuits, the principle is general and can be applied to any type of circuit technology in integrated or discrete implementation. Moreover, the methods are easily automated as the principles are based on closed-form formulae as opposed to iterative numerical search techniques.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.960
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.033
GPT teacher head0.247
Teacher spread0.214 · 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

Citations11
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Circuits and Systems I Regular PapersSame topicAnalog and Mixed-Signal Circuit DesignFrench-language works237,207