In the last few decades we have experienced a revolution in the field of electronics. Technology has been advanced in production of optimized power consumption and minimum area electronic circuits. During the past few years the concept of system-on-chip (SoC) has become an important segment in the market of integrated circuits. This development has increased not only the number of devices that can be put on a chip, but the chips now include both digital and analog circuits. Due to the current rapid growth of the portable electronics market such as wireless communication systems, battery-powered consumer electronics and implantable medical electronic devices, the demand for low-power analog and mixed-signal integrated circuits is increasing. Because of structured nature and high-level ionin digital circuits, level of automation has been kept in phase with development of this kind of circuits. However, this is not the case for analog circuits. The analog circuit design suffers from low-level ion and automation design and this makes it one of the major bottlenecks in mixed-signal design. Several analog design automation techniques have been reported in the literature. These methods can be categorized into two main groups, which are known as “knowledge-based” and “optimization-based” approaches. However, optimization-based methods can be ltr" Key Words: Analog Integrated Circuits, Automated Design, Evolutionary Strategy, Learnable Evolution Model, Multi Objective
