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Low-noise transimpedance amplifier designg procedure for optical communications

2014· article· en· W2087791643 on OpenAlexaff
Shahab Shahdoost, Bardia Bozorgzadeh, Ali Medi, Namdar Saniei

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Semiconductor Devices and Circuit Design
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsTransimpedance amplifierEffective input noise temperatureElectronic engineeringCMOSBandwidth (computing)Noise (video)Low-noise amplifierElectrical engineeringNoise figureCapacitive sensingPhotodiodeInstrumentation amplifierComputer scienceAmplifierElectronic circuitOperational amplifierEngineeringPhysicsOptoelectronicsTelecommunications

Abstract

fetched live from OpenAlex

Here, we report on design and measurement results of a state of the art low-noise and high-gain transimpedance amplifier (TIA) implemented in 0.18 μm TSMC CMOS technology. In depth design methodology for design of high gain and low noise TIA for 2.5 Gb/s optical communication family is presented. A novel noiseless capacitive feedback is proposed and implemented as a noise efficient feedback for TIA circuits. Besides, analytical noise calculations in this family of TIA circuits are presented and optimum noise criteria are derived. The saturation and instability problem of TIA circuits resulted from DC dark current of the input photodiodes (PDs) is addressed and a circuit-level solution is proposed. The measurement results of 0.18 μm chip shows bandwidth of 52 kHz to 1.62 GHz, and transimpedance gain of 75.5 dBΩ while dissipating 26.3 mW from a 2.2 V power supply, including the output buffer. Taking advantage of proposed capacitive feedback network and optimum noise criteria, noise measurement results show average input referred current noise of 3.18 pA/√Hz for this TIA in the bandwidth of operation.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.952
Threshold uncertainty score0.496

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 head0.029
GPT teacher head0.266
Teacher spread0.237 · 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
GenreMethods

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

Citations9
Published2014
Admission routes1
Has abstractyes

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