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A run-to-run controller for product surface quality improvement

机译:连续运行的控制器可改善产品表面质量

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摘要

In semiconductor manufacturing, the surface quality of silicon wafers has a significant impact on the subsequent processes that produce devices using the wafers as a component. The surface quality of a wafer is characterised by a two-dimensional (2-D) data structure: the geometric requirement for the wafer surface is smooth and flat and the thickness should fall within certain specification limits. Therefore, both low deviation and high uniformity are desirable for control over the wafer quality. In this work, we develop a run-to-run control algorithm for improving wafer quality. Considering the unique 2-D data structure, we first construct a model that encompasses the spatial correlation of the observations on the wafer surface to link the wafer quality with the process variables, and subsequently develop a recursive algorithm to generate optimal set points for the controllable factors. More specifically, a Gaussian-Kriging model is used to characterise the spatial dependence of the thickness measures of the wafer and a recursive least square method is employed to update the estimates of the model parameters. The performance of the new controller is studied via simulation and compared with existing controllers, which demonstrates that the newly proposed controller can effectively reduce the surface variations of the silicon wafers.
机译:在半导体制造中,硅晶片的表面质量对以晶片为组件生产器件的后续工艺产生重大影响。晶片的表面质量以二维(2-D)数据结构为特征:晶片表面的几何要求是光滑且平坦的,并且厚度应在某些规格范围内。因此,低偏差和高均匀性对于控制晶片质量都是理想的。在这项工作中,我们开发了运行到运行的控制算法以提高晶片质量。考虑到独特的二维数据结构,我们首先构建一个模型,该模型包含晶片表面上观察值的空间相关性,以将晶片质量与工艺变量联系起来,然后开发一种递归算法以生成可控的最佳设定点因素。更具体地说,使用高斯-克里格模型来表征晶片的厚度度量的空间依赖性,并且采用递归最小二乘法来更新模型参数的估计。通过仿真研究了新控制器的性能,并与现有控制器进行了比较,证明新提出的控制器可以有效减少硅晶片的表面变化。

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