An Optimal Design of Metamaterial Window Based on Neural Networks
Compared with solid-state devices,vacuum electronic devices can provide higher power and wider bandwidth.As an important component,the microwave window is used to isolate the internal and external environment of the vacuum electronic device,maintain the high vacuum inside the device,and transmit high-energy electromagnetic wave.At present,the performance of traditional microwave window constrains the performance of broadband high-frequency vacuum devices.Therefore,a design method of metamaterial structure using neural network algorithm is proposed which can realize a broadband micro-wave metamaterial window with low reflection and low voltage standing wave ratio.The nonlinear fitting effect of neural network is used to establish a mapping relationship between the window structure and its spectrum,thus avoiding the complicated solving process of Maxwell's equations,improving the simulation speed of spectrum performance and simplifying the design process.By designing and training the bidirec-tional neural network,the reverse design of window is achieved,by which the structural parameters can be generated only by inputting the corresponding target spectral characteristic parameters.
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