Program for the optimization of an OTA for front end electronics based on multi objective genetic algorithms

Abstract

The design of an interface to a specific sensor induces costs and design time mainly related to the analog part. So to reduce these costs it should have been standardized like digital electronics. The aim of the present work is the elaboration of a method based on multi objectives genetic algorithms (MOGAS) to allow automated synthesis of analog and mixed systems. This proposed methodology is used to find the optimal dimensional transistor parameters (length and width) in order to obtain operational amplifier performances for analog and mixed CMOS (complementary metal oxide semi-conductor)-based circuit applications. Six performances are considered in this study, DC gain, unity-gain bandwidth, phase margin, power consumption, area, and slew rate. We used the Matlab optimization toolbox to implement the program. Also by using variables obtained from genetic algorithms, the operational transconductance amplifier (OTA) is simulated by using Cadence Virtuoso Spectre circuit simulator in standard TSMC RF 0.18μm CMOS technology. A good agreement is observed between the program optimization and electric simulation.

Multiobjective Genetic Algorithms Program for the Optimization of an OTA for Front-End Electronics

“…The differences between the work [9] and our case are that transistors of the current mirror ( 3 and 4) do not have the same size. Table 3 presents results in comparison with our design.…”

“…Accuracy, ease of use, generality, robustness, and reasonable run-time are necessary for a circuit synthesis solution to gain acceptance by using optimization methods [4][5][6][7][8][9].…”

Telescopic Op-Amp Optimization for MDAC Circuit Design

“…Therefore, the use of multiple-objective optimization algorithms (MOGAs) is of a great importance to the automatic design of Op-Amp. Accuracy, ease of use, generality, robustness, and reasonable run-time are all necessary for a circuit synthesis solution to gain acceptance by using optimization methods [6][7][8]. This method uses a program based on multi-objective optimization using a genetic algorithm (GA) to calculate the optimal transistors dimensions (length and width) of transistors, of an Op-Amp which is used in the Multiplying Digital-to-Analog Converter (MDAC).…”