基于神经网络的雪崩光电二极管SPICE模型构建

SPICE Model Construction of Avalanche Photodiode Based on Neural Network

针对雪崩光电二极管(Avalanche Photodiode,APD)雪崩前后电流数量级相差大、I-V特性曲线变化剧烈的特点,在对I-V特性数据进行对数化、归一化预处理的基础上,采用浅层神经网络完成I-V函数拟合,并进一步优化神经网络结构以提升模型准确性.在此基础上,使用Verilog-A硬件描述语言实现APD的SPICE模型,并应用Cadence软件设计电路验证模型的有效性和准确性,引入相对误差评估模型的准确度.结果表明:优化后的神经网络学习的I-V特性函数与TCAD仿真数据的均方误差损失为2.544×10^(-7),SPICE模型验证电路采样数据与TCAD仿真数据的最大相对误差为3.448%,平均相对误差为0.630%,构建SPICE模型用时约50 h,实现了高精度、高效率的器件SPICE模型构建,对新型APD的设计与应用具有重要指导意义.

To address the current of avalanche photodiodes(APD) with large differences in orders of magnitude before and after the avalanche and drastic changes in I-V characteristic curves,a shallow neural network is used to complete the I-V function fitting based on logarithmic and normalized pre-processing of I-V characteristic data,and the neural network structure is further optimized to improve the model accuracy.On this basis,the SPICE model of APD is implemented in Verilog-A hardware description language,the validity and accuracy of the model are verified by designing the circuit in Cadence,and the relative error is introduced to evaluate the model accuracy.The results show that the mean square error loss of the I-V characteristic function learned by the optimized neural network and the TCAD simulation data is 2.544×10^(-7),the maximum relative error of the SPICE model verification circuit and the TCAD simulation data is 3.448%,the average relative error is 0.630%,and the time spent to construct the SPICE model is about 50 hours.High-precision and high-efficiency device SPICE model construction are realized,which has important guiding significance for the design and application of new APDs.