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Record W2085806145 · doi:10.1109/tdmr.2005.853502

Effects of hot-carrier stress on the performance of CMOS low-noise amplifiers

2005· article· en· W2085806145 on OpenAlexafffund
Sasan Naseh, M. Jamal Deen, Chih‐Hung Chen

Bibliographic record

VenueIEEE Transactions on Device and Materials Reliability · 2005
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Semiconductor Devices and Circuit Design
Canadian institutionsMcMaster University
FundersCMC Microsystems
KeywordsTransconductanceCMOSMaterials scienceElectrical engineeringTransistorOptoelectronicsAmplifierLow-noise amplifierNoise figureBiasingLinearityElectronic engineeringVoltageEngineering

Abstract

fetched live from OpenAlex

The effects of direct current (dc) hot-carrier stress on the characteristics of NMOSFETs and a fully integrated low-noise amplifier (LNA) made of NMOSFETs in an 0.18-/spl mu/m complementary MOS (CMOS) technology are investigated. The increase in threshold voltage and decrease in mobility caused by hot carriers lead to a drop in the biasing current of the transistors. These effects lead to a decrease in the transconductance and an increase of the output conductance of the device. No measurable change in the parasitic gate-source and gate-drain capacitances in the devices under test were observed due to hot carriers. In the LNA, the important effects caused by hot carriers were a drop of the power gain and an increase of the noise figure. A slight increase in the input and output matching S/sub 11/ and S/sub 22/, respectively, after hot-carrier stress was observed. The linearity parameter IIP3 of the LNA improved after stress. This is believed to be due to the improvement of the linearity of the I-V characteristics of the transistors in the LNA at the particular operating point where the measurements were performed.

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)0.002
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.216
Teacher spread0.207 · 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

Citations39
Published2005
Admission routes2
Has abstractyes

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