Design methodology for low power RF LNA based on the figure of merit and the inversion coefficient

Fadhuile, François; Taris, Thierry; Deval, Yann; Enz, Christian; Belot, Didier

doi:10.1109/icecs.2014.7050026

Cited by 6 publications

(2 citation statements)

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“…Also, to minimize the consumption, in (6), C 1 must be equal to C 2 . The required values of C 1 and C 2 can then be calculated using (4).…”

Section: Linear Analysismentioning

confidence: 99%

“…In our methodology, we use simple analytical equations that enable us to find easily the multiple design variables, and then with a figure of merit (FoM), we find the best trade-off between current consumption and phase noise. A similar version of this methodology has been applied to Low-Noise Amplifiers (LNAs) in [4]. The methodology is based on the inversion coefficient (IC) [5], which is the drain current I D , normalized to the specific current I spec ,…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design of ultra low‐power RF oscillators based on the inversion coefficient methodology using BSIM6 model

Guitton

Mangla

Chalkiadaki

et al. 2015

Circuit Theory & Apps

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SUMMARYThis paper presents a fast and accurate way to design and optimize LC oscillators using the inversion coefficient (IC). This methodology consists of four steps: linear analysis, nonlinear analysis, phase noise analysis, and optimization using a figure of merit. For given amplitude of oscillation and frequency, we are able to determine all the design variables in order to get the best trade-off between current consumption and phase noise. This methodology is demonstrated through the design of Pierce and cross-coupled oscillators and has been verified with BSIM6 metal oxide semiconductor field effect transistor compact model using the parameters of a commercial advanced 40 nm complementary metal oxide semiconductor process.

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“…Also, to minimize the consumption, in (6), C 1 must be equal to C 2 . The required values of C 1 and C 2 can then be calculated using (4).…”

Section: Linear Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%