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Record W2132581913 · doi:10.1109/icvd.2004.1260901

Comparative study of low voltage OTA designs

2004· article· en· W2132581913 on OpenAlexaff
Deyasini Majumdar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTransconductanceCMOSElectronic engineeringOperational amplifierOperational transconductance amplifierElectrical engineeringElectronic circuitTransistorBandwidth (computing)EngineeringVoltageAmplifierComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

The increased demand for Low Power/Low Voltage (LP/LV) circuits while maintaining high bandwidth and dynamic range, has led to widespread use of current-mode techniques in the design of analog circuits. Current trends in continual scaling down of transistor gate lengths and reduction in supply voltage have further added to the design challenges. This paper deals with the design of building blocks for current-mode circuit design, namely, Operational Transconductance Amplifier (OTA). Designs of three OTAs have been discussed. These OTAs operate at supply voltages about 3.3 V. Simulations have been carried out in Cadence (with Hspice) for Taiwan Semiconductor Manufacturing Corporation's (TSMC's) 0.35 /spl mu/m CMOS process. The main aim of this paper is to present and compare new OTA designs, which have potential for use in LV applications. One of the proposed OTAs has further been fabricated and tested for real-time application. The application addressed is that of designing spectral shaping filters for use in Second Generation High-bit-rate Digital Subscriber Line Technology (HDSL-2). The relative performance of all the three OTAs has also been presented.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.042
GPT teacher head0.254
Teacher spread0.212 · 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

Citations5
Published2004
Admission routes1
Has abstractyes

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