基于布谷鳥搜索算法的雙π電感模型參數(shù)優(yōu)化
發(fā)布時間：2018-11-12 12:05
【摘要】：提出了一種新的基于布谷鳥搜索算法的片上電感模型參數(shù)優(yōu)化算法。布谷鳥搜索算法對復(fù)雜非線性非凸函數(shù)的優(yōu)化效果顯著,這種函數(shù)的特點與半導(dǎo)體器件緊湊型模型參數(shù)和優(yōu)化目標(biāo)之間的非線性特性相近。該算法開發(fā)了模型參數(shù)交叉操作,將兩個解空間的對應(yīng)參數(shù)交換位置,避免敏感度高的參數(shù)對敏感度低的參數(shù)的過度影響。引入了自適應(yīng)參數(shù),使算法的搜索步長能自適應(yīng)調(diào)整,既不會因為步長太大跳過最優(yōu)解,也不會因為步長太小導(dǎo)致收斂速度慢。采用集成電路工藝片上螺旋電感的實測數(shù)據(jù)對該算法進行驗證,得到較好的擬合度。提出的模型優(yōu)化算法可適用于集成電路器件模型的自動優(yōu)化。
[Abstract]:A new algorithm for optimizing the parameters of on-chip inductance model based on Cuckoo search algorithm is proposed. The Cuckoo search algorithm has a remarkable effect on the optimization of complex nonlinear nonconvex functions, and the characteristics of this function are similar to the nonlinear characteristics between the parameters of the compact model of semiconductor devices and the optimization objectives. In this algorithm, the cross-operation of model parameters is developed to exchange the corresponding parameters between the two solution spaces so as to avoid the excessive influence of the highly sensitive parameters on the low-sensitivity parameters. By introducing adaptive parameters, the search step size of the algorithm can be adjusted adaptively, neither because the step size is too large to skip the optimal solution, nor because the step size is too small to cause the convergence speed to be slow. The algorithm is verified by the measured data of spiral inductance on chip of integrated circuit, and a good fit is obtained. The proposed model optimization algorithm can be applied to the automatic optimization of integrated circuit device models.
【作者單位】： 杭州電子科技大學(xué)射頻電路與系統(tǒng)教育部重點實驗室;吉林醫(yī)藥學(xué)院生物醫(yī)學(xué)工程學(xué)院;
【基金】：國家自然科學(xué)基金資助項目(61331006)
【分類號】：TN303;TP18
本文編號：2327059
[Abstract]:A new algorithm for optimizing the parameters of on-chip inductance model based on Cuckoo search algorithm is proposed. The Cuckoo search algorithm has a remarkable effect on the optimization of complex nonlinear nonconvex functions, and the characteristics of this function are similar to the nonlinear characteristics between the parameters of the compact model of semiconductor devices and the optimization objectives. In this algorithm, the cross-operation of model parameters is developed to exchange the corresponding parameters between the two solution spaces so as to avoid the excessive influence of the highly sensitive parameters on the low-sensitivity parameters. By introducing adaptive parameters, the search step size of the algorithm can be adjusted adaptively, neither because the step size is too large to skip the optimal solution, nor because the step size is too small to cause the convergence speed to be slow. The algorithm is verified by the measured data of spiral inductance on chip of integrated circuit, and a good fit is obtained. The proposed model optimization algorithm can be applied to the automatic optimization of integrated circuit device models.
【作者單位】： 杭州電子科技大學(xué)射頻電路與系統(tǒng)教育部重點實驗室;吉林醫(yī)藥學(xué)院生物醫(yī)學(xué)工程學(xué)院;
【基金】：國家自然科學(xué)基金資助項目(61331006)
【分類號】：TN303;TP18
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