Accelerating Parameter Extraction of Power MOSFET Models Using Automatic Differentiation
Accelerating Parameter Extraction of Power MOSFET Models Using Automatic Differentiation
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DOI：
10.1109/tpel.2021.3118057
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发表时间：
2021-10
影响因子：
6.7
通讯作者：
Michihiro Shintani;Aoi Ueda;Takashi Sato
中科院分区：
文献类型：
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作者：
Michihiro Shintani;Aoi Ueda;Takashi Sato
模型参数的提取与紧凑模型本身的开发同样重要，因为仿真精度完全取决于所使用的参数的精度。提出了一种高效的功率金属氧化物半导体场效应晶体管（mosfet）紧凑模型参数提取方法。所提出的方法采用自动微分（AD），这是广泛用于训练人工神经网络。在提出的基于AD的参数提取中，通过形成便于误差向后传播的图来解析地计算所有模型参数的梯度。基于计算的梯度，计算密集的数值微分被消除，模型参数被有效地优化。使用具有13个模型参数的功率MOSFET模型进行实验以拟合市售碳化硅MOSFET的电流和电容特性。结果表明，该方法可以成功地获得模型参数比传统的数值微分方法快3.50倍，而达到相同的精度。
The extraction of the model parameters is as important as the development of compact model itself because simulation accuracy is fully determined by the accuracy of the parameters used. This article proposes an efficient model-parameter extraction method for compact models of power metal-oxide semiconductor field-effect transistors (mosfets). The proposed method employs automatic differentiation (AD), which is extensively used for training artificial neural networks. In the proposed AD-based parameter extraction, gradient of all the model parameters is analytically calculated by forming a graph that facilitates the backward propagation of errors. Based on the calculated gradient, computationally intensive numerical differentiation is eliminated and the model parameters are efficiently optimized. Experiments are conducted to fit current and capacitance characteristics of commercially available silicon carbide mosfet using power mosfet models having 13 model parameters. Results demonstrated that the proposed method could successfully derive the model parameters 3.50× faster than a conventional numerical-differentiation method while achieving the equal accuracy.