The integrated circuit (IC) market growth has been explosive over the last decade. The design of complex ICs has been possible due to technology improvements and design automation tools. The design automation of analog integrated circuit is inevitable considering recently emerging discipline such as System on Chip (SOC) design. The design of the analog portion of a mixed-signal integrated circuit often requires large fraction of the overall design time, despite the fact that the analog circuit is often relatively a small portion of the overall circuit. This brings about the absolute necessity to develop Computer Aided Design (CAD) tools to automate the analog circuit synthesis process. There is a need for high-performance analog electronic circuit satisfying large number of specifications. An achievement of fast, low-power, and low-area integrated circuits is the major requirement in the electronic industry.
The Operational Amplifier (OpAmp) is the most versatile and widely used building blocks in analog electronics. The CMOS OpAmp design problem is a complex and tedious task, which requires many compromises to be made between conflicting objectives. The performance of an OpAmp is characterized by number of parameters, such as, gain, power, slew rate, and area. These performance parameters are determined by transistor dimensions, bias current, and compensation capacitance values. The synthesis of CMOS OpAmp is translated to a multi-objective optimization task. Evolutionary algorithm is applied for the OpAmp design because of the multi-objective nature of the design problem and large search space. In this chapter, synthesis of CMOS OpAmp circuit using Weighted Sum Genetic Algorithm (WSGA) and Nondominated Sorting Genetic Algorithm (NSGA-II) is investigated. Experimentation is carried out for Miller Operational Transconductance Amplifier (OTA) and Folded cascode OpAmp architecture.