{"title":"基于 Verilog-A 的改进型人工神经网络建模应用于氮化镓 HEMT","authors":"Anwar Jarndal, Md Hasnain Ansari, Kassen Dautov, Eqab Almajali, Yogesh Singh Chauhan, Sohaib Majzoub, Soliman A. Mahmoud, Talal Bonny","doi":"10.1002/adts.202400645","DOIUrl":null,"url":null,"abstract":"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. 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引用次数: 0
摘要
本研究提出了一种新方法,通过集成 Verilog-A 编码,在 Keysight ADS 中实现用于模拟高电子迁移率晶体管 (HEMT) 的人工神经网络 (ANN) 模型。它简化了具有不同复杂性和层结构特征的 ANN 模型的实现过程。通过为两种不同基底上的 GaN HEMT 开发非线性模型,演示了所提出的方法。硅基氮化镓和碳化硅基氮化镓具有各自的栅极宽度,在栅极和漏极偏置条件下通过 S 参数进行表征。从去嵌入式 S 参数中提取固有栅极电容和电导,然后对其进行积分,以求得栅极电荷和电流。具有固有自热和陷波效应的漏极电流是根据在定义明确的静态电压下进行的脉冲 IV 测量建立模型的。随后,这些非线性元素的相关 ANN 模型相互连接,形成大信号模型的内在部分。然后,使用基于 Verilog-A 的代码完全实现这一包含所有 ANN 子模型的内在部分。然后通过单音和双音射频大信号测量对整个 ANN 大信号模型进行验证,结果表明该模型具有完美的拟合度和较高的收敛速度。使用所开发的基于 Verilog-A 的 ANN 代替基于表格的模型,整体仿真时间缩短了五倍。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 模型表现出更高的性能提升。这表明 Verilog-A 集成技术在非线性 GaN HEMT 建模中的实际可行性。
Improved Verilog‐A Based Artificial Neural Network Modeling Applied to GaN HEMTs
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.
期刊介绍:
Advanced Theory and Simulations is an interdisciplinary, international, English-language journal that publishes high-quality scientific results focusing on the development and application of theoretical methods, modeling and simulation approaches in all natural science and medicine areas, including:
materials, chemistry, condensed matter physics
engineering, energy
life science, biology, medicine
atmospheric/environmental science, climate science
planetary science, astronomy, cosmology
method development, numerical methods, statistics