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Inverse Lithography OPC Correction with Multiple Patterning and Etch Awareness

机译:具有多重图案和蚀刻意识的反光刻OPC校正

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In advanced nodes, the extension of DUV lithography deep into the sub-wavelength dimensions has led to exploration of many new Resolution Enhancement Techniques (RET). Generally speaking, these RET have enabled higher resolution capabilities using the same exposure wavelength, but at the cost of increasingly complex mask optimization process. One such technique applied to perform Optical Proximity Correction (OPC) is called Inverse Lithography Technique (ILT). It promises the best possible theoretical mask design by solving the inverse problem, where the optical transform from mask to wafer image is solved in reverse using a rigorous mathematical approach [1]. Although the benefits and potentials of ILT in producing a single exposure mask are well documented [2], its implementation in multiple patterning OPC (MP-OPC) is less explored. In this paper, an ILT mask optimization is applied on a metal layer, consisting of 3 exposures in a litho-etch x 3 (LELELE) process flow. It demonstrates the application of both multi-exposure and etch awareness within the ILT mask correction scheme. This is accomplished by including inter-layer constraints for the resist and the post-etch contours in the objective function of the ILT optimization. The ability to reduce potential inter-exposure failure modes as well as the associated increase in computational resources will be assessed. Additionally, the results will be compared against a conventional model-based OPC with similar multi-exposure and etch awareness.
机译:在高级节点中,DUV光刻技术扩展到亚波长尺寸深处,导致人们探索了许多新的分辨率增强技术(RET)。一般而言,这些RET使用相同的曝光波长实现了更高分辨率的功能,但是却以越来越复杂的掩模优化过程为代价。一种用于执行光学邻近校正(OPC)的技术称为反光刻技术(ILT)。它可以通过解决反问题来实现最佳的理论掩模设计,其中使用严格的数学方法逆解决了从掩模到晶圆图像的光学转换[1]。尽管ILT在生产单个曝光掩模方面的好处和潜力已得到充分文献证明[2],但在多图案OPC(MP-OPC)中的实现方式却鲜有探索。在本文中,在金属层上应用了ILT掩模优化,该层由光刻3倍(LELELE)工艺流程中的3次曝光组成。它演示了ILT掩模校正方案中多重曝光和蚀刻识别的应用。这是通过在ILT优化的目标函数中包括针对抗蚀剂和蚀刻后轮廓的层间约束来实现的。将评估减少潜在的曝光间故障模式的能力以及相关的计算资源增加。此外,将结果与具有类似多重曝光和蚀刻意识的传统基于模型的OPC进行比较。

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