深度学习的亚波长窄带陷波滤光片设计

亚波长光栅结构表现出优异的陷波滤光特性,其经典设计是设定亚波长的几何结构参数,求解麦克斯韦方程组,设定优化算法求解出最优解,需要消耗大量的时间和计算资源.提出一种基于深度学习的逆向设计方法,搭建了可以同时实现正向模拟与逆向设计的串联神经网络.基于python语言的Tensorflow库进行网络搭建;优化均匀波导层的高度...

Design of Subwavelength Narrow Band Notch Filter Based on Depth Learning

Subwavelength grating structures exhibit excellent notch filtering properties. The classical design is to find the optimal solution by setting the geometric structure parameters of the subwavelength, solving Maxwell’s equations, and setting an optimization algorithm. It consumes a lot of time and computing resources. This paper presents an inverse design method based on deep learning and constructs a series neural network which can realize both forward simulation and inverse design. The Tensorflow library based on Python language is constructed to optimize the height of uniform waveguide layer, the height of sub-wavelength grating, refractive index, period and duty cycle, and to study the characteristics of sub-wavelength grating notch filtering in the range of 0.45~0.7 μm. Using rigorous coupled wave analysis (RCWA) numerical simulation to generate 23 100 data sets, 18 480 data sets were randomly selected as training sets, and 4 620 data sets were used as test sets, the network node and Batch were studied. The results show that the network performance is best when the network model structure is 5×50×200×500×200×26, and the Batch size is 128 after 1 000 iterations. Compared with the independent network model, the loss rate of the forward simulation test set of the series neural network decreased from 0.033 63 to 0.004 5, and that of the reverse design decreased from 0.702 98 to 0.052 98. At the same time, the problem that the network can not converge in the reverse design process caused by the non-uniqueness of data is solved. The geometric structure parameters of the sub-wavelength grating are given in 1.35 s on average by inputting any spectral curve into the trained network, and the correlation between the parameters and the RCWA numerical simulation curve is analyzed, the similarity coefficients of the curves were all greater than 0.658 1, which belonged to strong correlation. In addition, a red, green and blue notch filter is designed, whose peak reflectivity can reach 98.91%, 99.98% and 99.88% respectively. Compared with the traditional method, this method can quickly and accurately calculate the geometric parameters of the grating. It provides a new method for sub-wavelength grating design.