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Convolution-variation separation method for efficient modeling of optical lithography

机译:卷积-变量分离方法用于光刻的高效建模

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We propose a general method called convolution-variation separation (CVS) to enable efficient optical imaging calculations without sacrificing accuracy when simulating images for a wide range of process variations. The CVS method is derived from first principles using a series expansion, which consists of a set of predetermined basis functions weighted by a set of predetermined expansion coefficients. The basis functions are independent of the process variations and thus may be computed and stored in advance, while the expansion coefficients depend only on the process variations. Optical image simulations for defocus and aberration variations with applications in robust inverse lithography technology and lens aberration metrology have demonstrated the main concept of the CVS method.
机译:我们提出一种称为卷积变化分离(CVS)的通用方法,以在模拟各种过程变化的图像时实现高效的光学成像计算,而不会牺牲精度。 CVS方法是从使用串联扩展的第一原理中得出的,该扩展由一组由一组预定扩展系数加权的预定基函数组成。基本函数与过程变化无关,因此可以预先计算和存储,而扩展系数仅取决于过程变化。用于散焦和像差变化的光学图像仿真以及在稳健的反光刻技术和镜头像差计量学中的应用证明了CVS方法的主要概念。

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