rapid top-down specification of an rf receiver using

Université de Mons Papy NDUNGIDI KIAZAYILA, Fortunato DUALIBE, Carlos VALDERRAMA FPMs - Service d‘Electronique et de Microélectronique (SEMi)

Rapid Top-Down Specification of an RF Receiver using Genetic Multi-Objective

Optimization Method

Introduction

Methodology

Results

Conclusion This work presents a novel approach to refine the parameters of an RF receiver to satisfy a standard

specification by using a genetic algorithm for multi-objective optimization. The interest of this approach is

to come rapidly up with a feasible specification avoiding a hard time-consuming iterative simulation

procedure. The methodology has been applied to the design of an LNA for complying with the IEEE

802.15.4 standard. In particular, it provided feasible design parameters for an inductively-degenerated

common-source cascode LNA with LC tank load, which was implemented on a CMOS 150nm process.

Compared to previously reported works, this approach shows a very low noise figure, acceptable power

consumption and a excellent trade-off between linearity and gain.

The design of RF receivers requires low power consumption, small

area, high performance and, at the same time, the shortest “time-to-

market”. To accelerate the design time, our work proposes:

To automate some calculations and transitions between design

steps;

To gain a significant time-saver by using the optimization instead

of the simulation in the specifications calculation phase;

To automate the circuit sizing of the receiver's blocks.

Standard (e.g. IEEE 802.15.4)

Performances comparison

Architecture (e.g. Homodyne)

Parameters IEEE 802.15.4 (PHY 2.4 GHz)

Frequency range 2400-2483.5 MHz

Channel band width 2 MHz

Channel spacing 5 MHz

Coverage Worldwide

Spreading technique DSSS

Modulation O-QPSQ with MSK

Data rate 250 kbit/s

Symbol rate 62.5 ksymbol/s

Number of channels 16

Sensitivity -85 dBm

Receiver maximum input level -20 dBm

PER 1%

Adjacent channel rejection 0 dB

Alternate channel rejection 30 dB

Parameters IEEE 802.15.4 PHY 2.4 GHz

Eb/No 7.78 dB

SNRout - 1.14 dB

SNRin 25.82 dB

NF 26.96 dB

IM3 - 68 dBm

IIP3 - 44 dBm

DRin 65 dB

Sfs 13 dBm (1V over 50Ω)

Gmin 33 dB

GMax 100.24 dB

Sta

ndard

Sum

mary

Receiver specification

RF Filter LNA Mixer AGC Channel Filter Ampli AA Filter ADC

Gain

NF

Rec. Band

Order

Gain

NF

IIP3

IIP2

Gain

NF

IIP3

NF

IIP3

GainMax

Gainmin

Gain

NF

Band width

Order

Gain

NF

IIP3

IIP1

Gain

NF

fsampling

Dynamic

Order

Resolution

IIP3, …

Antagonist global parameters : F and IIP3 Better noise factor (Fmin) Increase the Gains

Better linearity (IIP3Max) Decrease the Gains

Layout of the LNA (LF 150 nm Technology)

IIP3Total Max NFTotal Min

NFTotal (dB) 7.60 2.54

IIP3Total (dBm) 3.83 -2.78

GLNA (dB) 7.57 8.72

GMIX (dB) 3.49 16.50

GCF (dB) -1.5 -1.5

GAGC (dB) 22.44 25.1

GAAF (dB) -1.5 -1.5

NFLNA (dB) 1.25 1.1

NFMIX (dB) 6.77 5.61

NFCF (dB) 1.5 1.5

NFAGC (dB) 15.42 15.55

NFAAF (dB) 1.5 1.5

IIP3LNA (dBm) 4.98 4.78

IIP3MIX (dBm) 18 12.61

IIP3CF (dBm) 19.93 19.72

IIP3AGC (dBm) 18.22 16.46

IIP3AAF (dBm) 21.94 21.8

[1] [2] [3] This work

F (GHz) 2.4 2.4 2. 4 2.4

CMOS Technology 0.35 µm 0.25 µm 0.09 µm 0.15 µm

G (dB) 19.8 20 20 10.5

NF (dB) 3 2.4 4.65 1.7

IIP3 (dBm) 4.5 -3.4 -15.5 4.36

P1dB (dBm) -4.7 -21 -23.2 -2.7

Pc (mW) 22.4 4.8 0.24 8.7

[*] Christophe VERSÈLE, "Contribution à l'optimisation multiobjectif de convertisseurs électroniques de puissance. Application aux convertisseurs continu–continu isolés", PhD thesis, University of Mons,

Belgium, 2012.

[1] J.C. Huang, Ro-Min Weng, Hsiao Chih-Lung, and Kun-Yi Lin. "A 2 V 2.4 GHz fully integrated CMOS LNA with Q-ehhancement circuit". In Asia-Pacific Microwave Conference, pages 1028-1031, 2001.

[2] Xiaomin Yang, Thomas Wu, and John McMacken. "Design of LNA at 2.4 GHz using 0.25 µm technology". In Topical Meeting on Silicon Monolithic IC in Radiofrequence Systems, pages 12-17, 2001.

[3] Nils Kåre Midtflå, "A 2.4 GHz Ultra-Low-Power Low-Noise- Amplifier", Master thesis, Norwegian University of Science and Technology - Department of Electronics and Telecommunications, 2010.

RF frequency 2.4 GHz

S21 10.5 dB

S11 -17.7 dB

S22 -13.3dB

NF 1.7 dB

IIP3 4.36 dBm

Supply 1.8 V

Power Consumption 8.7 mW

Area (including pads) 1.06 mm x 0.898 mm

Frequency 2.44175 GHz

Voltage 1.8 V

Curant 5 mA

LL 1.3 nH

CL 3.26 pF

LM1,M2 0.15 um

WM1,2 768.5 um

NFMIN 1.24

LS 0.34 nH

LG 4.97 nH

Reuse of existing blocs

User and technology constraints

Example of analog design flow

Functional

Architectural

Structural

“Electrical”

Physical

Vout = A(V+ - V- )

Architectural exploration

Topology choice

Sizing

Layout

Input stage + N gain stages +

ESD protection circuit + …

Schematic: differential pair ,

current mirror

Choice of technology and sizing

Layout and fabrication

Gain ? Bandwidth ? Power ? T° ?

Traditional methodology Proposed methodology

requires a lot of time (in order of hours or days),

because it needs parametric simulation;

leads to hundreds of solutions (blocs

parameters) , not all of them are achievable

is very time-saver because optimization

takes a few seconds;

yields optimal and realistic blocs parameters

by taking into account the constraints

imposed by designers without any simulation

The b

ounds o

f

realis

tic v

alu

es

Blocs Gain (dB) NF (dB) IIP3 (dBm)

RF filter -1.5 1.5 13.42 to 22

LNA 5 to20 1 to 4.4 -22 to 6

Mixer 0 to 17.5 5 to 25 -12 to 24

Channel filter -1.5 1.5 13.42 to 22

AGC 5 to 40 5 to 35 4.38 to 20

AAF -1.5 1.5 13.42 to 22

We have solved a nonlinear multi-objective optimization

problem with constraints, by using the genetic algorithm

NSGA-II. Currently, this algorithm is one of the most powerful

used for optimizing a large number of engineering problems.

Its effectiveness was proven through many scientific

publications [*]

Each solution is composed by different blocs parameters values

(GLNA, GMIX, GCF, GAGC, GAAF, NFLNA, NFMIX, NfCF, NFAGC, NFAAF,

IIP3LNA, IIP3MIX, IIP3CF, IIP3AGC, AIP3AAF.)

An algorithm has been implemented for obtaining feasible design parameters of an inductively-degenerated

common-source cascode Low Noise Amplifier (LNA) from a chosen solution.

For choosing a solution in the Pareto front, the user can be refine or filter the set of solution taking into account

the reality of each design (Design strategies). For example: keep only the solutions that have a minimum noise

figure for LNA and a maximum linearity for the mixer, etc.

The NSGA-II algorithm is based on the biological evolution

theory of natural selection.

From a set of initial solutions, the algorithm randomly selects

some of these solutions to produce another set called new

generation. Through the successive generations, the set of

solutions evolves towards an optimum that minimizes the cost

functions (both noise figure and linearity, in this case).

At each step, the algorithm creates a new generation from the

current one by applying three types of operations:

the selection, for choosing some solutions from the current

set to produce a new set of solutions;

the crossover, for combining two current solutions to create

the next generation;

the mutation, for applying random changes to current

solutions to form new solutions;

Elapsed time is 8.516682 seconds

The Matlab© Optimization

tool (Optimtool) can be

used to optimize both, NF

and IIP3, cost functions.

The Pareto front represents a

set of non-dominated solutions

ZOOM IN on 2 solutions

Post-layout simulations results

LN

A s

pe

cific

atio

n