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Model based CAOPC flow for memory chips to improve performance and consistency of RET solutions

机译:用于存储芯片的基于模型的CAOPC流程可提高RET解决方案的性能和一致性

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Key factors for maximizing yield in a modern semiconductor fab for Memory device manufacturing include wafer critical dimension uniformity and accuracy control. Resolution Enhancement Techniques (RET) solutions for the highly repetitive arrayed memory devices have been driven by the need for perfect geometric consistency without compromising the lithographic quality. Traditionally, both optical proximity correction (OPC) and sub-resolution assist features (SRAFs) insertion for these repetitive cell-array structures have been dealt by applying manual hand-crafted or rule-based methods. But these can be prone to iterative human intervention, long runtimes and sub-par lithographic quality. This work adopts a pattern/property aware approach (PA)2 and cell-array OPC technology that leverage the inherent repetitive and hierarchical structure of the cell-array to ensure the lithographic quality and perfect geometric consistency and symmetry down to the level of feature edges with model-based OPC and rule-based SRAF solutions. The flow also demonstrates a drastic reduction in runtime and turn-around-time to mask tapeouts for the full chip (core and periphery).
机译:在用于存储设备制造的现代半导体晶圆厂中,最大化成品率的关键因素包括晶圆关键尺寸的均匀性和精度控制。用于高度重复的阵列存储设备的分辨率增强技术(RET)解决方案是由对完美的几何一致性的需求所推动的,而又不影响光刻质量。传统上,这些重复性细胞阵列结构的光学邻近校正(OPC)和次分辨率辅助特征(SRAF)插入都是通过手工制作或基于规则的方法完成的。但是,这些可能容易受到人工干预,运行时间长和低于标准的光刻质量。这项工作采用了模式/属性感知方法(PA)2和单元阵列OPC技术,该技术利用了单元阵列固有的重复性和层次结构来确保光刻质量以及完美的几何一致性和对称性,直至特征边缘的水平基于模型的OPC和基于规则的SRAF解决方案。该流程还演示了大幅减少了运行时间和周转时间,以掩盖整个芯片(核心和外围设备)的流片。

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