2019
DOI: 10.1109/tcsi.2019.2914502
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Design Methodology Based on the Inversion Coefficient and its Application to Inductorless LNA Implementations

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Cited by 16 publications
(22 citation statements)
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“…The proposed LNA achieves the highest voltage gain in HP-mode with [16], and among the lowest power consumption (350uW) in LP-Mode with [8][9] [10]. We observe the limitation on the power consumption strongly impacts the noise figure, which remains above 4dB for a PDC below 600uW [8][9][10][11][12][13]. Interestingly, the noise figure limitation seems to be technology independent.…”
Section: Measurement Resultsmentioning
confidence: 99%
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“…The proposed LNA achieves the highest voltage gain in HP-mode with [16], and among the lowest power consumption (350uW) in LP-Mode with [8][9] [10]. We observe the limitation on the power consumption strongly impacts the noise figure, which remains above 4dB for a PDC below 600uW [8][9][10][11][12][13]. Interestingly, the noise figure limitation seems to be technology independent.…”
Section: Measurement Resultsmentioning
confidence: 99%
“…The MOS transistor described with the IC operates in WI when IC<0.1, in the Moderate Inversion (MI) when 0.1<IC<10, and in SI when IC>10. The small signal model of a MOS transistor, reported in Appendix [12], is used to exploit the analytic derivations (1), ( 2) and (3). To explore the design space of the circuit, the algorithm illustrated in Fig.…”
Section: A Ekv Model and Design Space Explorationmentioning
confidence: 99%
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“…Despite its low number of components, optimizing these kinds of LNA present some difficulties that diverse methodologies try to overcome by different strategies ranging from analytical expressions [11] to synthesis algorithms [12] or intermediate approaches [13].…”
Section: Introductionmentioning
confidence: 99%
“…The proposed methodology employs contour maps in a different approach than usual. While the typical use of these graphics implies representing the gradient of a single parameter versus two variables [13]- [15]; contour maps from this work sacrifice gradient data to specification limit from several parameters. This work is organized as follows.…”
Section: Introductionmentioning
confidence: 99%