LowArea/LowPower CMOS OpAmps Design Based on Total Optimality Index Using Reinforcement Learning Approach

This paper presents the application of reinforcement learning in automatic analog IC design. In this work, the MultiObjective approach by Learning Automata is evaluated for accommodating required functionalities and performance specifications considering optimal minimizing of MOSFETs area and power consumption for two famous CMOS opamps. The results show the ability of the proposed method to optimize aforementioned objectives, compared with three MO wellknown algorithms (including Particle Swarm Optimization, Inclined Planes system Optimization, and Genetic Algorithm). So that for a twostage CMOS opamp, it is obtained 560.42 μW power and 72.825 〖μm〗^2 area, and power 214.15 μW and area 13.76 〖μm〗^2 for a singleended foldedcascode opamp. In addition to evaluating the Paretofronts obtained based on Overall Nondominated Vector Generation and Spacing criteria, in terms of Total Optimality Index, MOLA for both cases has been able to have the best performance between the applied methods, and other researches with values of 25.683 and 34.16 dB, respectively.