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Constraint multi-objective automated synthesis for CMOS operational amplifier

机译:CMOS运算放大器的约束多目标自动合成

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A multi-objective evolution algorithm (MOEA) is presented to automatically determine the parameters in Op-Amp synthesis where the cost functions (e.g., minimizing the power dissipation and the chip area) and the constraint functions (e.g., the user-defined specifications) can be modeled as polynomials of the design variables. The proposed algorithm is based on MOEA which does not use weighting coefficients in converting multiple objectives into single objective. A constraint handling strategy without penalty parameters is proposed to avoid the difficulty of penalty parameter selection. Moreover, an elitist maintaining scheme is utilized to keep the evenness of the Pareto front. Simulations over several benchmark functions validate the efficiency of the proposed algorithm for the evenness of population distribution and the convergence to the Pareto front. Numerical experiments of a Miller compensated two-stage Op-Amp show that the proposed MOEA is able to achieve better performance than NSGA-II + PCH, GA+SPF and GA+PCH.
机译:提出了一种多目标进化算法(MOEA),以自动确定运算放大器合成中的参数,其中成本函数(例如,最小化功耗和芯片面积)和约束函数(例如,用户定义的规格)可以建模为设计变量的多项式。所提出的算法基于MOEA,在将多个目标转换为单个目标时不使用加权系数。为了避免惩罚参数选择的困难,提出了一种没有惩罚参数的约束处理策略。而且,采用精英维护方案来保持帕累托阵线的均匀性。通过对几个基准函数的仿真,验证了所提出算法在总体分布均匀性和Pareto前沿收敛性方面的效率。 Miller补偿的两级运算放大器的数值实验表明,提出的MOEA能够实现比NSGA-II + PCH,GA + SPF和GA + PCH更好的性能。

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