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首页> 外文期刊>Microelectronic Engineering >Optimizations of a photoresist coating process for photolithography in wafer manufacture via a radial basis neural network: A case study
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Optimizations of a photoresist coating process for photolithography in wafer manufacture via a radial basis neural network: A case study

机译：通过径向基神经网络优化晶圆制造中光刻的光刻胶涂层工艺：案例研究

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摘要

This investigation applied a hybrid method, which combined a trained radial basis network (RBN) [S. Chen, C.F.N. Cowan, P.M. Grant. Orthogonal least squares learning algorithm for radial basis function networks. Neural Networks 2(2) (1991), 302-309] and a sequential quadratic programming (SQP) method [R. Fletcher, Practical Methods of Optimizations, vol. 1, Unconstrained Optimization, and vol. 2, Constrained Optimization, John Wiley and Sons Inc., New York, 1981], to determine an optimal parameter setting for photoresist (PR) coating processes of photolithography in wafer manufacture. Nine experimental runs based on an orthogonal array table were utilized to train the RBN and the SQP method was applied to search for an optimal setting. An orthogonal array table provided an economical and systematic arrangement of experiments to map the relationship between controlled parameters and desired outputs. In this study, a mean thickness and non-uniformity of the thickness of the PR were selected as monitored quality targets for the PR coating process. In addition, the PR temperature, the chamber humidity, the spinning rate, and the dispensation rate were four controlled parameters. The PR temperature and the chamber humidity were found to be the most significant factors in the mean thickness and non-uniformity of the thickness for the PR coating process from the analysis of variance (ANOVA).
机译：这项研究应用了一种混合方法，该方法结合了经过训练的径向基网络（RBN）[S. Chen C.F.N.科文（PM）格兰特径向基函数网络的正交最小二乘学习算法。 Neural Networks 2（2）（1991），302-309]和顺序二次规划（SQP）方法[R.弗莱彻，实用的优化方法，第1，无约束优化和卷。 2，约束优化，John Wiley and Sons Inc.，纽约，1981年]，以确定晶片制造中光刻的光刻胶（PR）涂层工艺的最佳参数设置。利用基于正交数组表的九个实验运行来训练RBN，并应用SQP方法来搜索最佳设置。正交数组表提供了一种经济，系统的实验安排，以映射受控参数和所需输出之间的关系。在这项研究中，选择PR的平均厚度和厚度不均匀性作为PR涂层工艺的监测质量目标。另外，PR温度，室湿度，纺丝速度和分配速度是四个控制参数。根据方差分析（ANOVA），发现PR温度和反应室湿度是影响PR涂层工艺平均厚度和厚度不均匀性的最重要因素。

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