论文标题
具有深神网络方法的纳米光量波长反式电流的逆设计
Inverse design of nano-photonic wavelength demultiplexer with a deep neural network approach
论文作者
论文摘要
在本文中，我们提出了一个预训练的综合神经网络（PTCN），作为对集成光子电路的逆设计的全面解决方案。通过使用联合培训过程利用最初的预训练的逆和正向模型，我们的PTCN模型表现出对训练数据数量和质量的显着耐受性。作为概念证明的证明，波长反复的逆设计用于验证PTCN模型的有效性。即使训练数据的大小减少到17％，提出的PTCN模型预测的相关系数仍大于0.974。实验结果表明，与预测有很好的一致性，并证明了具有超紧凑型占地面积的波长反复传动器，高传输效率，传输损失为-2dB，低反射-10dB和围绕-7dB的低串扰。
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.
