GaN HEMT 器件上的铁电材料滞后效应导致的分数阶电容行为

IF 1.6 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC International Journal of Numerical Modelling-Electronic Networks Devices and Fields Pub Date : 2024-01-15 DOI:10.1002/jnm.3206
Dariskhem Pyngrope, Shubhankar Majumdar, Giovanni Crupi
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引用次数: 0

摘要

近年来,氮化镓(GaN)高电子迁移率晶体管(HEMT)因其在大功率和高频率应用方面的卓越性能而跻身半导体行业的前沿。准确的电容建模对于优化性能和促进高能效电子电路设计至关重要。为了反映氮化镓 HEMT 中氮化铝钪(AlScN)栅电容的复杂性质,本研究调查了基于分数阶微积分的独特 Grünwald-Letnikov 模型的使用情况。由于分数阶导数可对非整数阶系统进行建模,因此所提出的模型为准确表征电容提供了一种强有力的方法。通过将模型的预测结果与实验数据进行比较,利用各种误差指标(包括平均绝对误差 (MAE)、均方根误差 (RMSE)、最大百分比误差 (MPE)、平均绝对百分比误差 (MAPE) 和均方误差 (MSE))对 Grünwald-Letnikov 模型的准确性进行了定量评估。值得注意的是,该模型在误差指标上表现出显著的一致性,前向扫描的最大值为 MPE = 0.21%、MAE = 0.05%、MAPE = 0.33%、MSE = 0.01% 和 RMSE = 0.09%,后向扫描的最大值为 MPE = 0.32%、MAE = 0.04%、MAPE = 0.39%、MSE = 0.01% 和 RMSE = 0.08%。这些指标肯定了模型在捕捉 GaN HEMT 器件细微电容特性方面的精确性。因此,通过分数阶微积分增强的新型 Grünwald-Letnikov 模型首次被证明是精确描述 GaN HEMT 电容特性的可靠工具。该模型能够无缝地解释使用铁电材料所带来的复杂性,凸显了它在推进半导体设计和优化器件性能方面的潜力。
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Fractional order capacitance behavior due to hysteresis effect of ferroelectric material on GaN HEMT devices

In recent years, gallium nitride (GaN) high electron mobility transistors (HEMTs) have come to the forefront of the semiconductor industry because of their exceptional performance in both high-power and high-frequency utility. Accurate capacitance modeling is crucial to optimize performance and facilitate energy-efficient electronic circuit design. In order to reflect the complex nature of the aluminum scandium nitride (AlScN) gate capacitance in GaN HEMTs this study investigates the use of the unique Grünwald-Letnikov model based on fractional order calculus. The proposed model presents a powerful approach to accurately characterize capacitance since fractional order derivatives allow modeling of non-integer order systems. Quantitative assessment of the Grünwald-Letnikov model's accuracy is performed through various error metrics, including mean absolute error (MAE), root mean square error (RMSE), maximum percentage error (MPE), mean absolute percentage error (MAPE), and mean squared error (MSE), by comparing the model's predictions to experimental data. Notably, this model demonstrates remarkable consistency in error metrics, with maximum values of MPE = 0.21%, MAE = 0.05%, MAPE = 0.33%, MSE = 0.01%, and RMSE = 0.09% for the forward scan, and MPE = 0.32%, MAE = 0.04%, MAPE = 0.39%, MSE = 0.01%, and RMSE = 0.08% for the backward scan. These metrics affirm the model's precision in capturing the nuanced capacitance characteristics of GaN HEMT devices. Hence, herein for the first time, the novel Grünwald-Letnikov model, augmented by fractional order calculus, proves to be a robust tool for accurately characterizing GaN HEMT capacitance. Its ability to seamlessly account for the complexities introduced by using ferroelectric material highlights its potential for advancing semiconductor design and optimizing device performance.

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来源期刊
CiteScore
4.60
自引率
6.20%
发文量
101
审稿时长
>12 weeks
期刊介绍: Prediction through modelling forms the basis of engineering design. The computational power at the fingertips of the professional engineer is increasing enormously and techniques for computer simulation are changing rapidly. Engineers need models which relate to their design area and which are adaptable to new design concepts. They also need efficient and friendly ways of presenting, viewing and transmitting the data associated with their models. The International Journal of Numerical Modelling: Electronic Networks, Devices and Fields provides a communication vehicle for numerical modelling methods and data preparation methods associated with electrical and electronic circuits and fields. It concentrates on numerical modelling rather than abstract numerical mathematics. Contributions on numerical modelling will cover the entire subject of electrical and electronic engineering. They will range from electrical distribution networks to integrated circuits on VLSI design, and from static electric and magnetic fields through microwaves to optical design. They will also include the use of electrical networks as a modelling medium.
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