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Record W1593333072 · doi:10.1002/9783527631506.ch9

Implementation of Analog Integrated Circuit Filters

2010· other· en· W1593333072 on OpenAlexaff
R. Raut, M.N.S. Swamy

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicAdvanced Adaptive Filtering Techniques
Canadian institutionsConcordia University
Fundersnot available
KeywordsElectronic engineeringFilter (signal processing)Analogue filterElectrical engineeringActive filterCMOSMixed-signal integrated circuitComputer scienceEngineeringIntegrated circuitDigital filter

Abstract

fetched live from OpenAlex

This chapter introduces the basic principles of implementation of integrated circuit (IC) analog filters. It briefly describes the active devices that are available in an IC technology. The chapter discusses considerations related to the implementation of resistance and capacitance in a typical complementary metal-oxide-semiconductor (CMOS) IC technology. It summarizes the examples of analog filter implemented in a known IC technology. For a preliminary design of the filter, the active device is assumed to have ideal characteristics. An important problem in the IC environment is the coupling of noise signal from nearby circuit nodes and especially from the neighboring digital subsystems in an analog-digital mixed-mode VLSI system. The chapter presents the case of a high-frequency, high-Q BP filter implemented using a BiCMOS technological process. The filter can be used as an intermediate frequency filter in GSM cellular telephones.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.136
Threshold uncertainty score0.993

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)0.0080.000

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 head0.012
GPT teacher head0.261
Teacher spread0.249 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2010
Admission routes1
Has abstractyes

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