A new method for optimization of analog integrated circuits using pareto-based multi-objective genetic algorithm
Abbas Golmakani, Khalil Mafinejad, Abbas Z. Kouzani · Deakin Research Online (Deakin University) · 2009
This paper presents a new tnethod for design and optimization of analog integrated circuits based on Pareto-based A1uW··Objective Genetic Algorithm (MOCA). The efficiency of the method is evaluated by using benchmark problems and compared with other MOCA algorithms. The method is implemented and used to optimize a telescopic cascade Op-Amp. Here, transistor sizes, compensation capacitor and bias voltages are determined by Genetic Algorithm (CA). Moreover, the circuils that are formed, using the components of the determined values, are simulated with Ihpice. The output parameters, such as gain, bandwidth, phase margin and pmver are extracted from the generated output file, and the area of chip is calculated separately. The extracted output parameters are used as costfimctionsfor creating next generation in GA. Finally, a set of Pareto-front which satisfies the conditions of the problem is introduced. This enables the circuit designer to select the best solution from the set. This algorithm is implemented in A1atlab and is simulated hy using I-Ispice and 1SkfC 0, J 8um CMOS technology is employed in simulation. Copyright © 2009 Praise Worthy Prize S.r.!. - All rights reserved.