Title
Design of Radio-Frequency Integrated CMOS Discrete Tuning Varactors Using the Particle Swarm Optimization Algorithm
Abstract
This paper presents an automated design procedure of radio- frequency integrated CMOS discrete tuning varactors (RFDTVs). This new method use the maximin and the particle swarm optimization (PSO) algorithms to promote well distributed non-dominated fronts in the parameters space when a single-objective function is optimized. The fitness function used in the search tool is proportional to the RFDTV quality factor. The outcome of the automated design method comprises a set of RFDTV circuits, all having the same maximum performance. Each solution, which corresponds to one RFDTV circuit, is defined by the number of cells and by the circuit components values. This approach allows the designer to choose among several possible circuits the one that is easier to implement in a given CMOS process. To validate the effectiveness of the synthesis procedure proposed in this paper (PSO-method) comparisons with a design method based on genetic algorithms (GA-method) are presented. A 0.18 μ m CMOS radio-frequency binary-weighted differential switched capacitor array (RFDSCA) was designed and implemented (the RFDSCA is one of the possible topologies of the RFDTVs). The results show that both design methods are in very good agreement. However, the PSO technique outperforms the GA-method in the design procedure run time taken to accomplish the same performance results.
Year
DOI
Venue
2009
10.1007/978-3-642-02481-8_184
IWANN (2)
Keywords
Field
DocType
radio frequency integrated circuit,parameter space,analog circuits,design method,fitness function,genetic algorithm,switched capacitor,particle swarm optimization,radio frequency,objective function,quality factor
Particle swarm optimization,Computer science,Algorithm,Network topology,CMOS,Radio frequency,Fitness function,Design methods,Electronic circuit,Genetic algorithm
Conference
Volume
ISSN
Citations 
5518
0302-9743
0
PageRank 
References 
Authors
0.34
12
6