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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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对基于有效平面化长度的芯片级CMP预测模型的图形参数提取算法的优化.

Bowen Ren1,2, Lan Chen1, Rong Chen1

  • 1The EDA Center, Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China.

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概括
此摘要是机器生成的。

化学机械抛光 (CMP) 面临着影响芯片厚度的模式效应的挑战. 这项研究引入了密度校正和线路接触变形配置文件,以增强CMP预测模型,显著提高准确性.

关键词:
在HKMG中,HKMG是HKMG.密度校正 密度校正 密度校正压缩尺度的CMP模型有效平面化长度 (EPL)布局提取 抽取 布局提取取决于布局的效应.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 半导体制造业 半导体制造业
  • 计算机建模 计算建模

背景情况:

  • 化学机械抛光 (CMP) 对于半导体平面化至关重要,但由于材料厚度的模式依赖变化而受到影响.
  • 这些变化,称为布局依赖效应 (LDE),显著影响集成电路性能和制造产量.
  • 目前用于预测CMP后形态的模型通常依赖于图形参数提取,这不足以捕捉复杂的布局模式交互.

研究的目的:

  • 通过解决现有的布局依赖效应 (LDE) 模型的局限性,开发一种改进的方法来预测CMP后芯片形态.
  • 通过结合新的密度校正和权重功能来提高CMP预测模型的准确性.
  • 根据实验数据验证拟议的优化方法的有效性.

主要方法:

  • 计算平均密度作为密度校正因子,以考虑模式效应.
  • 引入了一维线接触变形形状作为加权函数,以更好地描述模式相互作用.
  • 将密度校正方法应用于基于密度阶段高度的高K金属门-CMP预测模型.
  • 对比表面预测结果与没有优化与数据相比.

主要成果:

  • 优化的CMP预测模型显示,预测错误显著减少.
  • 在氧化物高度预测中,平均平方误差 (MSE) 降低了40.1%.
  • 与预优化结果相比, (Al) 高度预测的MSE降低了35.2%.
  • 通过直接比较与实验数据验证了改进的准确性.

结论:

  • 拟议的密度校正和线路接触变形概况有效地增强了CMP预测模型.
  • 优化方法显著提高了预测CMP后芯片形态的准确性.
  • 这一进步对于半导体行业的设计验证和制造开发至关重要.