applied sciences Review Inverse Design for Silicon Photonics: From Iterative Optimization Algorithms to Deep Neural Networks Simei Mao 1,2, Lirong Cheng 1,2 , Caiyue Zhao 1,2, Faisal Nadeem Khan 2, Qian Li 3 and H. Y. Fu 1,2,* 1 Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518055, China;
[email protected] (S.M.);
[email protected] (L.C.);
[email protected] (C.Z.) 2 Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China;
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[email protected]; Tel.: +86-755-3688-1498 Abstract: Silicon photonics is a low-cost and versatile platform for various applications. For design of silicon photonic devices, the light-material interaction within its complex subwavelength geometry is difficult to investigate analytically and therefore numerical simulations are majorly adopted. To make the design process more time-efficient and to improve the device performance to its physical limits, various methods have been proposed over the past few years to manipulate the geometries of silicon platform for specific applications. In this review paper, we summarize the design methodologies for silicon photonics including iterative optimization algorithms and deep neural networks. In case of iterative optimization methods, we discuss them in different scenarios in the sequence of increased degrees of freedom: empirical structure, QR-code like structure and irregular structure. We also review inverse design approaches assisted by deep neural networks, which generate multiple devices Citation: Mao, S.; Cheng, L.; Zhao, with similar structure much faster than iterative optimization methods and are thus suitable in C.; Khan, F.N.; Li, Q.; Fu, H.Y.