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机器学习反向方法用于封装材料的弹性塑料构成参数
Mingqi Gao1,2, Tong Hu1, Yagang Zhang1
1School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 611731, China.
Nanomaterials (Basel, Switzerland)
|February 12, 2026
概括
这项研究提出了一种新的方法,用于使用纳米缩和神经网络准确测量微电子材料的特性. 该技术实现了对弹性塑料参数的高精度,这对于3D异构集成至关重要.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算科学 计算科学
背景情况:
- 精确测量材料力学参数对于3D异构集成中的工艺质量和产品可靠性至关重要.
- 目前的方法在精确确定微电子材料的弹性塑料非线性构成参数方面面临挑战.
研究的目的:
- 为微电子材料的弹性塑料非线性构成参数开发一种高精度的测量方法.
- 为了利用纳米沉积测试,神经网络和先进的材料表征算法.
主要方法:
- 为材料响应参数构建了一个基于神经网络的前向表征模型.
- 设计了一个改进的代算法,用于向前模型的反向解决方案.
- 应用最小平方法来解决过度确定方程,增强稳定性和独特性.
主要成果:
- 实现了对弹性塑料构成参数的高精度和快速反向解决方案.
- 材料参数的相对误差<3% (95% CI),最大误差<8%.
- 关键内参数的反向收误差<0.1%.
结论:
- 开发的方法提供了微电子材料力学参数的准确和可靠的测量.
- 通过有限元模拟验证了测量参数对Through Ceramic Via (TCV) 产品工艺应力的影响.
- 这种技术对于推进3D异构集成技术至关重要.
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