Inverse design of high efficiency and large bandwidth power splitter for arbitrary power ratio based on deep residual network

被引:0
作者
Jin Wen
Zhengwei Wu
Hui Zhang
Qian Wang
Huimin Yu
Ying Zhang
Yu Pan
Zhanzhi Liu
机构
[1] Xi’an Shiyou University,School of Science
[2] Chinese Academy of Sciences,State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics
来源
Optical and Quantum Electronics | / 56卷
关键词
Power splitter; Inverse design; Direct binary search; Neural network;
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中图分类号
学科分类号
摘要
In this research, we propose the deep Residual Network to realize the inverse design of a low loss 1 × 3 port power splitter with an area of 2.6 × 2.6 μm2 on a standard silicon-on-insulator platform. Then the area is used as the inverse design region and discretized into 20 × 20 square pixels, where each pixel can be switched between the two random initial states of silicon square with and without holes. Besides, we use the direct binary search algorithm to change the state of the pixels so that the distribution of all pixels in the inverse design region reaches the optimal value of the algorithm. While training the network, inputting spectral transmission response, and using the etched hole vector positions as a label for the inverse design, it achieved an accuracy of 0.9111 and a correlation coefficient greater than 0.88 for all three ports. Finally, we demonstrated 1 × 3 power splitters with 1:2:1, 1:2:1.5, 1:3:1, and 1:3:2 distribution ratios and a more than 90% maximum transmission efficiency with bandwidth from 1450 to 1650 nm while having a low insertion loss of less than 0.45 dB. This research can be found potential applications in the design of photonic devices with high performance and small size.
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  • [1] Brunetti G(2021)Design of a large bandwidth 2× 2 interferometric switching cell based on a sub-wavelength grating J. Opt. 23 085801-1-085801-14
  • [2] Marocco G(2014)Optical crosspoint matrix using broadband resonant switches IEEE J. Sel. Top. Quantum Electron. 20 1-10
  • [3] Di Benedetto A(2022)Deep inverse photonic design: a tutorial Photon. Nanostruct. Fundam. Appl. 52 101070-1-101070-16
  • [4] Giorgio A(2022)Hybrid inverse design of photonic structures by combining optimization methods with neural networks Photon. Nanostruct. Fundam. Appl. 52 101073-1-101073-8
  • [5] Armenise MN(2022)Feature-based machine learning for the efficient design of nanophotonic structures Photon. Nanostruct. Fundam. Appl. 52 101077-1-101077-7
  • [6] Ciminelli C(2021)Machine learning and applications in ultrafast photonics Nat. Photon. 15 91-101
  • [7] DasMahapatra P(2022)Neural network-based surrogate model for inverse design of metasurfaces Photon. Res. 10 1462-1471
  • [8] Stabile R(2014)Resolution-enhanced all-optical analog-to-digital converter employing cascade optical quantization operation Opt. Express 22 21441-21453
  • [9] Rohit A(2022)Experimental demonstration of inverse-designed silicon integrated photonic power splitters Nanophotonics 11 4581-4590
  • [10] Williams KA(2023)Inverse design of an on-chip optical response predictor enabled by a deep neural network Opt. Express 31 2049-2060