Paper Info
Title
Particle Swarm Optimization Design of Low-Power Multistage Amplifier using gm/ID Methodology.
Abstract
A design flow using the gm/ID methodology with the adaptive particle swarm optimization (PSO) algorithm is proposed for the modern analog circuit in this paper. For the advanced CMOS process, gm/ID methodology is suitable to the long channel and short channel design in all transistor operation regions. Different from the classical PSO algorithm, the adaptive PSO algorithm features the better search efficiency and faster convergence speed over the global search. Two amplifiers were designed and implemented in a standard 0.11μm CMOS process using MATLAB and HSPICE. Using the thermal noise coefficient γ and the corner frequency fcorner, this paper explored the noise design budget of low-power multistage amplifier in different saturation modes. Detailed optimization of the objective function and constraints are classified into the mono-objective case and the multi-objective case. The total running times of simulations are 5649 s and 6813 s while the errors are less than 9% and 10%, respectively. Compared with CODE, GA+PF and DE+PF algorithms, it can save more running time and improve the accuracy of the design. Moreover, it provides more design freedom for the trade-off among gain, the gain-bandwidth (GBW) product, noise and the phase margin under worst cases without extra tweaking. Not only can the methodology work in the 0.18μm CMOS process, but also be migrated to the 0.11μm CMOS process, even in the nanometer analog circuit.
Year
Venue
Field
2016
Journal of Circuits, Systems, and Computers
Particle swarm optimization,MATLAB,Control theory,Computer science,Noise (electronics),Electronic engineering,Design flow,Phase margin,Transistor,Amplifier,Multistage amplifier
DocType
Volume
Issue
Journal
25
9
Citations 
PageRank 
References 
0
0.34
11
Authors
5
Name
Order
Citations
PageRank
Gengyu Zhang100.34
Xia Xiao2164.73
Jiangtao Xu37418.98
Kaiming Nie4358.77
Zhiyuan Gao5179.39