Inverse Design of High-Dimensional Nanostructured 2×2 Optical Processors Based On Deep Convolutional Neural Networks

End-to-End Optimization for a Compact Optical Neural Network Based on Nanostructured 2 × 2 Optical Processors

“…The accurate transfer matrix is calculated by S-parameter sweep in 3D Finite-Difference Time-Domain (FDTD) software from Lumerical Solutions, Inc. As shown in Fig. 1(b), we propose a compact optical processor on a standard siliconon-insulator (SOI) platform with a 220-nm-thick silicon and a silica cladding layer for protection [10]. The proposed optical processor is composed of two input ports, two output ports, and a middle digitized meta-structure formed by a set of nanoholes with a specific distribution.…”

“…A range of functional components based on compact digitized metastructures has been designed [6][7][8][9]. Digitized meta-structures not only can be designed to implement a specific function but have been proved to serve as an optical processor to achieve the bar state, cross state, and unitary transmission [10]. 2×2 silicon photonic components have been arranged in waveguide circuit architecture to realize diverse computational functionalities [11], demonstrating the potential in computing and communications applications.…”

“…Besides, a deep neural network (DNN) can be applied to the design of nanophotonic devices due to its capability to learn the complex relationship between optical responses and device geometries. Nevertheless, the training of neural networks tends to be accompanied by issues such as taking a long time, demanding a large data set, high training difficulty, and overfitting [10,14].…”

“…However, due to the discrete decision of the optimization process caused by the meta-structures, it is not possible to optimize the devices 2 > REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) < through an explicit functional relation. In traditional optimization schemes, the first step is to obtain the transfer matrix corresponding to the device through weight matrix decomposition [2], and in the second step, the corresponding device can be optimized from the transfer matrix [10]. However, the inverse optimization from the optical response to the physical structure is hard to get the target solution [14,16], and the error accumulation is not linear.…”