机器学习模型用于有效地表征Schottky屏障光二极管内部参数
Richard O Ocaya1, Andronicus A Akinyelu2, Abdullah G Al-Sehemi3,4,5
1Department of Physics, University of the Free State, P. Bag X13, Phuthaditjhaba, 9866, South Africa. ocayaro@ufs.ac.za.
Scientific reports
|August 26, 2023
概括
人工神经网络 (ANN) 模型可以准确地预测Schottky光二极管参数,而无需了解热电辐射. 这些机器学习模型显著减少了设备开发中的分析时间.
科学领域:
- 半导体设备物理学 半导体设备物理
- 材料科学是一种材料科学.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 肖特基屏障二极管 (SBD) 是重要的半导体设备.
- 精确提取内部参数,如屏障高度和理想性因子,对于设备优化至关重要.
- 传统方法通常依赖于复杂的物理模型,例如热电离辐射 (TE) 方程,这可能是计算密集的.
研究的目的:
- 开发和验证基于人工神经网络 (ANN) 的模型,用于分析和提取Schottky光二极管 (SPD) 的内部参数.
- 为了证明这些模型在没有先前了解非线性热电离辐射 (TE) 方程的情况下能够预测设备参数的能力.
- 评估拟议的机器学习 (ML) 方法在设备表征中的准确性和效率.
主要方法:
- 在36个数据集上训练和评估ML模型,这些数据集来自氧化石墨烯 (GO) 杂的p-Si SBDs.
- 使用在不同的GO兴奋剂水平 (0-10%) 和照明强度 (0-30mW/cm2) 下的电流-电压-温度-照明数据.
- 将独立计算的参数 (障碍高度,理想系数,连续电阻) 纳入训练数据,并在更高强度和外部兴奋剂水平下预测参数.
主要成果:
- 在所有数据集中,ANN模型实现了高精度,平均平方误差和平均绝对误差低于0.003.
- 这些模型成功地预测了内部参数,而没有明确依赖TE方程.
- 准确的预测在模型开发数据和在不同的条件下外部数据集上都得到了证明.
结论:
- 基于ANN的模型有效地捕捉光二极管反应,并准确地预测内部SBD参数.
- 拟议的ML方法可显著减少设备开发周期的分析时间.
- 这些模型显示了在各种科学和工程领域对各种数据集的广泛应用的潜力.
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