This study presents a novel approach to implementing an artificial neural network (ANN) model for simulating high electron mobility transistors (HEMTs) in Keysight ADS through integrating Verilog‐A coding. It streamlines the realization of ANN models characterized by diverse complexities and layer structures. The proposed method is demonstrated by developing nonlinear models for GaN HEMT on two distinct substrates. GaN‐on‐Si and GaN‐on‐SiC with respective and gate widths are characterized by S‐parameters at a grid of gate and drain bias conditions. The intrinsic gate capacitance and conductances are extracted from the de‐embedded S‐parameters, which are then integrated to find the gate charges and currents. The drain current with the inherent self‐heating and trapping effects is modeled based on the pulsed IV measurement at well‐defined quiescent voltages. Subsequently, the related ANN models of these nonlinear elements are interconnected to form the intrinsic part of the large‐signal model. This intrinsic part with all ANN sub‐models is then completely implemented using a Verilog‐A‐based code. The whole ANN large‐signal model is then validated by single‐ and two‐tone radio frequency large‐signal measurements, which shows a perfect fitting with a high convergence rate. The overall simulation time is five times reduced when the developed Verilog‐A‐based ANN is used instead of the table‐based model. Overall, the large‐signal Verilog‐A‐based ANN model exhibits an improved performance enhancement compared to the conventional table‐based models. This indicates the practical viability of the Verilog‐A integration technique in modeling the nonlinear GaN HEMTs.

中文翻译：

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应用于 GaN HEMT 的改进的基于 Verilog-A 的人工神经网络建模


本研究提出了一种通过集成 Verilog-A 编码来实现人工神经网络（ANN）模型的新方法，用于在 Keysight ADS 中模拟高电子迁移率晶体管（HEMT）。它简化了以不同复杂性和层结构为特征的 ANN 模型的实现。通过在两种不同的衬底上开发 GaN HEMT 的非线性模型来证明所提出的方法。GaN-on-Si 和 GaN-on-SiC 具有各自的栅极宽度，在栅极网格和漏极偏置条件下通过 S 参数来表征。从去嵌入的 S 参数中提取本征栅极电容和电导，然后将其积分以找到栅极电荷和电流。具有固有自热和捕获效应的漏极电流是根据在明确定义的静态电压下的脉冲 IV 测量建模的。随后，这些非线性元件的相关 ANN 模型相互连接，形成大信号模型的本征部分。然后，这个包含所有 ANN 子模型的固有部分完全使用基于 Verilog-A 的代码实现。然后通过单音和双音射频大信号测量验证整个 ANN 大信号模型，结果显示完美拟合，收敛率高。当使用开发的基于 Verilog-A 的 ANN 而不是基于表格的模型时，整体仿真时间缩短了 5 倍。总体而言，与传统的基于表格的模型相比，基于 Verilog-A 的大信号 ANN 模型表现出更好的性能增强。这表明 Verilog-A 集成技术在对非线性 GaN HEMT 进行建模方面的实际可行性。