Lin Zhang, Longqin Xie, Zihan Xiang, Zhongmao Cai, Yatai Gao, Weifeng Jiang. Design of Silicon Hybrid Multiplexer/Demultiplexer Based on Deep Neural Network[J]. Acta Optica Sinica, 2025, 45(7): 0713002
- Acta Optica Sinica
- Vol. 45, Issue 7, 0713002 (2025)
Fig. 1. Silicon hybrid multiplexer/demultiplexer. (a) Schematic diagram; (b) structure parameters and material platform
Fig. 2. Architecture of DNN-based inverse-design platform for silicon hybrid multiplexer/demultiplexer
Fig. 3. Optimization process of DNN model. (a) Relationship between the minimum validation loss and the number of neurons for different hidden layers when the epoch is 30000 times; (b) relationship between the number of epochs and validation loss
Fig. 4. Training results of neural network for inverse design: relationship between loss value and epoch
Fig. 5. Propagating mode fields of inverse-designed silicon hybrid multiplexer/demultiplexer. (a) Inputting TM0 mode at port I1; (b) inputting TE0 mode at port I1; (c) inputting TE0 mode at port I2
Fig. 6. Rlationships between transmission and operating wavelength of silicon hybrid multiplexer/demultiplexer
Fig. 7. Relationships between transmission and operating wavelength of device for different fabrication tolerances. (a) Error is -5 nm; (b) error is +5 nm
Fig. 8. Pictures of experimentally fabricated silicon chip. (a) Referenced PDM-link and waveguide; (b) silicon hybrid multiplexer/demultiplexer and MDM-link; (c) MDM-link; (d) functional region; (e) enlarged image of functional region
Fig. 9. Normalized experimental results of silicon hybrid multiplexer for multiplexing/demultiplexing. (a) TM0 mode; (b) TE0 mode; (c) TE1 mode
Fig. 10. Inverse-design results with different expected transmittances. (a) TTE1=0.5; (b) TTE1=0.7; (c) TTE1=0.9
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Table 1. Comparison of footprint and performance of silicon hybrid multiplexer/demultiplexers
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