low-area/low-power cmos op-amps 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 multi-objective approach by learning automata is evaluated for accommodating required functionalities and performance specifications considering optimal minimizing the mosfets area and power consumption for two famous cmos op-amps. the results show the ability of the proposed method to optimize aforementioned objectives, compared with three mo well-known algorithms. so that, a power of 560.42 𝜇𝑊 and an area of 72.825 𝜇𝑚^2 are obtained for a two-stage cmos op-amp, and also a power of 214.15 𝜇𝑊 and an area of 13.76 𝜇𝑚^2 are obtained for a single-ended folded-cascode op-amp. in addition, in terms of total optimality index, mola for both cases has the best performance between the applied methods, and other research works with values of -25.683 and -34.162 db, respectively. the performance of the circuits is evaluated through hspice and the approach is implemented in matlab, so a combination of matlab and hspice is performed. the two-stage and single-ended folded-cascode op-amps are designed in 0.25μm and 0.18μm cmos technologies, respectively.