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Modelling and optimization of multilayer RF passives using coupled neural networks and genetic algorithms

机译:使用耦合神经网络和遗传算法对多层射频无源器件进行建模和优化

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We present the first combined neural network and genetic algorithm based modelling, design and optimization of multilayer passives. The embedded multilayer passives considered are of great interest for RF system-on-package for W-CDMA and C-band applications. An accurate neural network model for multilayer inductors and capacitors is developed using measured results for the frequency range of 1-5 GHz. The neural network model is used to perform sensitivity analysis and derive response surfaces. An innovative technique is then applied in which genetic algorithm based optimization is coupled with neural network modelling of electrical performance. The proposed neuro-genetic algorithm based design promises to minimize the time and cost for multilayer passive design while providing greater accuracy.
机译:我们提出了第一个结合神经网络和遗传算法的多层无源器件的建模,设计和优化方法。所考虑的嵌入式多层无源器件对于W-CDMA和C频段应用的RF封装系统非常感兴趣。利用在1-5 GHz频率范围内的测量结果,为多层电感器和电容器建立了精确的神经网络模型。神经网络模型用于执行灵敏度分析并导出响应面。然后应用一种创新技术,其中基于遗传算法的优化与电气性能的神经网络建模相结合。所提出的基于神经遗传算法的设计有望在提供更高准确性的同时,将多层无源设计的时间和成本降至最低。

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