Inverse design of nano-photonic wavelength demultiplexer with a deep neural network approach
This provides an efficient design method for integrated photonic circuits, though it appears incremental as it builds on existing neural network approaches.
The paper tackles the inverse design of a nano-photonic wavelength demultiplexer using a pre-trained-combined neural network (PTCN), achieving a correlation coefficient above 0.974 with reduced training data and demonstrating a device with -2dB transmission loss, -10dB reflection, and -7dB crosstalk.
In this paper, we propose a pre-trained-combined neural network (PTCN) as a comprehensive solution to the inverse design of an integrated photonic circuit. By utilizing both the initially pre-trained inverse and forward model with a joint training process, our PTCN model shows remarkable tolerance to the quantity and quality of the training data. As a proof of concept demonstration, the inverse design of a wavelength demultiplexer is used to verify the effectiveness of the PTCN model. The correlation coefficient of the prediction by the presented PTCN model remains greater than 0.974 even when the size of training data is decreased to 17%. The experimental results show a good agreement with predictions, and demonstrate a wavelength demultiplexer with an ultra-compact footprint, a high transmission efficiency with a transmission loss of -2dB, a low reflection of -10dB, and low crosstalk around -7dB simultaneously.