基于宽温度范围实验数据的肖特基二极管特性机器学习建模

IF 3.3 3区 物理与天体物理 Q2 PHYSICS, CONDENSED MATTER Superlattices and Microstructures Pub Date : 2021-12-01 DOI:10.1016/j.spmi.2021.107062
Yunis Torun , Hülya Doğan
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引用次数: 7

摘要

在本研究中,使用了4种常见的机器学习方法来模拟Au/Ni/n-GaN/未掺杂GaN肖特基二极管的I-V特性。测量了先前生产的Au/Ni/n-GaN/未掺杂GaN肖特基二极管在从温度40K到400K的电压下,以20K步长施加在二极管端子上的电流值。采用自适应神经模糊系统、人工神经网络、支持向量回归和高斯过程回归技术建立模型,实验数据共包含5192个样本。在确定每个模型的最小模型误差的组合和规格后,对得到的模型的各项性能指标进行比较。在学习阶段和测试阶段,ANFIS模型的性能都明显优于其他模型,RMSE模型误差分别为6.231e-06和6.806e-06。因此,它被认为是在40K和400K之间的所有温度值下建模I-V特性的强大工具。
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Modeling of Schottky diode characteristic by machine learning techniques based on experimental data with wide temperature range

In this study, 4 common machine learning methods have been used to model the I–V characteristic of the Au/Ni/n-GaN/undoped GaN Schottky diode. The current values of previously produced Au/Ni/n-GaN/undoped GaN Schottky diode against the voltages applied to the diode terminal starting from the temperature of 40K up to 400K with 20K steps were measured. Models were created using Adaptive Neuro Fuzzy System, Artificial Neural Network, Support Vector Regression, and Gaussian Process Regression techniques using experimental data containing 5192 samples in total. After determining the combinations and specifications for each one that provide the lowest model error of each model, the performances of the obtained models were compared with each other concerning the various performance indices. The performance of the ANFIS model was found to be much better than the others in both the learning and test phases with RMSE model errors as 6.231e-06 and 6.806e-06, respectively. Therefore, it was proposed as a powerful tool for modeling I–V characteristics at all temperature values between 40K and 400K.

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来源期刊
Superlattices and Microstructures
Superlattices and Microstructures 物理-物理:凝聚态物理
CiteScore
6.10
自引率
3.20%
发文量
35
审稿时长
2.8 months
期刊介绍: Micro and Nanostructures is a journal disseminating the science and technology of micro-structures and nano-structures in materials and their devices, including individual and collective use of semiconductors, metals and insulators for the exploitation of their unique properties. The journal hosts papers dealing with fundamental and applied experimental research as well as theoretical studies. Fields of interest, including emerging ones, cover: • Novel micro and nanostructures • Nanomaterials (nanowires, nanodots, 2D materials ) and devices • Synthetic heterostructures • Plasmonics • Micro and nano-defects in materials (semiconductor, metal and insulators) • Surfaces and interfaces of thin films In addition to Research Papers, the journal aims at publishing Topical Reviews providing insights into rapidly evolving or more mature fields. Written by leading researchers in their respective fields, those articles are commissioned by the Editorial Board. Formerly known as Superlattices and Microstructures, with a 2021 IF of 3.22 and 2021 CiteScore of 5.4
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