Neural network-based capacitance models are accurate, but some of them are not charge-conservative. In this work, a novel consistent gate charge model for GaN high electron mobility transistors is presented based on neural networks. The equivalent circuit parameters are extracted using the multiobjective gray wolf optimizer-based hybrid method, which improves the accuracy of parameter extraction. To obtain more reliable data sets for accurate neural network-based modeling, the outliers in the extracted intrinsic capacitances are automatically detected and removed using the isolation forest technique. The gate charge is obtained by integrating the capacitances with the voltages at different temperatures. A neural network is used to model the bias- and temperature-dependent gate charges, and the intrinsic capacitance formulation is obtained by taking the partial derivative of the gate charge function with respect to the voltages. The proposed model is charge-conservative and requires no transcapacitances. The large-signal model is implemented in the Advanced Design System and verified by small- and large-signal measurements. Good agreement is obtained between the measurements and simulations.

中文翻译：

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通过细化本征电容建立基于神经网络的准确 GaN HEMT 一致栅极电荷模型


基于神经网络的电容模型是准确的，但其中一些模型不具有电荷保守性。在这项工作中，提出了一种基于神经网络的 GaN 高电子迁移率晶体管的新型一致栅极电荷模型。采用基于多目标灰狼优化器的混合方法提取等效电路参数，提高了参数提取的准确性。为了获得更可靠的数据集以进行基于神经网络的精确建模，使用隔离森林技术自动检测和删除提取的固有电容中的异常值。栅极电荷是通过将电容与不同温度下的电压积分获得的。使用神经网络对与偏置和温度相关的栅极电荷进行建模，并通过对栅极电荷函数相对于电压求偏导数来获得本征电容公式。所提出的模型是电荷保守的并且不需要跨电容。大信号模型在先进设计系统中实现，并通过小信号和大信号测量进行验证。测量和模拟之间获得了良好的一致性。