一种半经验方法,用于校准半导体设备的模拟模型
Rahul Jaiswal1,2, Manel Martínez-Ramón2, Tito Busani3,4
1CHTM, University of New Mexico, 1313 Goddard St SE, Albuquerque, NM, 87106, USA.
Scientific reports
|June 27, 2023
概括
本研究介绍了一种混合方法,用于校准半导体设备模拟模型,使用最小的数据和机器学习. 这种方法提高了用于半导体优化的基于计算机的原型设计的效率.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
- 电气工程 电气工程
背景情况:
- 半导体设备优化传统上涉及耗时的制造和测试周期.
- 精确的模拟模型对于高效的原型设计至关重要,但需要精确校准材料参数.
- 校准通常需要大量的表征数据和专家知识,这构成了一个重要的瓶.
研究的目的:
- 开发一种用于校准多个半导体设备模拟模型的混合方法.
- 在模型校准过程中减少对广泛的表征数据和专家调整的依赖.
- 用太阳能电池作为案例研究来证明该方法的有效性.
主要方法:
- 建议采用混合方法,将最小特征数据与基于机器学习的预测模型相结合.
- 对工业制造的太阳能电池的光学和电气模拟模型进行了校准.
- 模拟设备的性能与物理设备的测量数据进行了比较.
主要成果:
- 混合方法成功校准了光伏设备的模拟模型.
- 校准模型准确地预测了太阳能电池的性能.
- 与传统方法相比,这种方法证明了时间和资源的效率.
结论:
- 拟议的混合方法为校准半导体设备模拟模型提供了一个有效的策略.
- 这种技术可以显著加速半导体研发中的基于计算机的原型设计.
- 最小的表征数据与机器学习相结合,可以有效地弥合模型校准的差距.
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