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预测建模和数据驱动优化CdFe2O4/p-Si异质连接的电气行为
Nejmeddine Yahyaoui1, Mokhtar Hjiri2, Slah Mansouri3
1Institut Préparatoire Aux Etudes d'Ingénieur de Sfax, Université de Sfax, Sfax 3000, Tunisia.
ACS omega
|October 20, 2025
概括
这项研究通过机器学习 (ML) 增强了CdFe2O4/p-Si二极管的分析. ML模型准确地预测了电气特性,改善了这些半导体设备的参数提取.
科学领域:
- 材料科学 材料科学 材料科学
- 电气工程 电气工程
- 计算科学 计算科学
背景情况:
- 铁 (CdFe2O4) 薄膜是通过sol-gel旋转涂层合成的.
- 这些薄膜沉积在经过化学处理的p型 (p-Si) 基板上.
- 结构分析显示多晶膜具有随机粒度分布.
研究的目的:
- 为了研究CdFe2O4/p-Si二极管的电气特性.
- 将实验数据与机器学习 (ML) 集成,以进行增强的分析.
- 将基于ML的参数提取与传统方法进行比较.
主要方法:
- 用于CdFe2O4薄膜沉积的Sol-gel旋转涂层.
- 使用Fytronix 9000半导体特性系统进行电气测量.
- 人工神经网络 (ANN) 和混合ML模型的应用用于数据解释.
主要成果:
- ML技术显著提高了参数提取的准确性和效率.
- 0.9999的确定系数 (R2) 表示实验值和预测值之间的完美相关性.
- ANN模型准确地确定了与实验数据相一致的最佳功率和过渡频率.
结论:
- 机器学习为分析复杂的半导体设备行为提供了一种强大的方法.
- ML提高了对CdFe2O4/p-Si二极管的电性质的理解和预测.
- 该研究验证了ML在半导体设备表征中的有效性.
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