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Performance Evaluation Considering Mask Misalignment in Multiple Patterning Decomposition

机译:考虑多个图案化分解中的掩模未对准性能评估

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As the technology advances to 14/10 nm technology nodes and beyond, multiple patterning lithography (MPL) is no longer an option but a necessity. For advanced technology nodes, process variations have significant influences on the quality of fabricated circuits, and often lead to unexpected power/timing degenerations. For example, a 6 nm misalignment causes a 15% error in coupling capacitance and a 5% error on total capacitance, whereas a 2 nm displacement creates approximately a 5% error for coupling capacitance and 2% error for total capacitance [1]. Mask misalignment would increase the coupling capacitance for a given layout and further complicate the way of simulating timing closure. In this paper, we studied the coupling capacitance variations due to mask misalignment in MPL, and mathematically proved that worst case coupling capacitance scenarios for all MPL decompositions. Our algorithm is able to identify a decomposition with a tight upper bound on the worst case coupling capacitance, which can be used in timing/power analysis. Our approach is expected to help the engineers to quickly evaluate the quality of different decompositions, and better understand the pros and cons brought by MPL decompositions.
机译:随着技术进入14/10nm技术节点及更远的,多个图案化光刻(MPL)不再是一个选择,而是必需品。对于先进的技术节点,过程变化对制造电路的质量产生了重大影响,并且通常导致意外的功率/定时退化。例如,6nm的未对准导致耦合电容的15%误差和总电容上的5%误差,而2nm位移产生约5%的耦合电容误差和总电容的2%误差[1]。掩模未对准将增加给定布局的耦合电容,并进一步复杂化模拟定时闭合的方式。在本文中,我们研究了由于MPL中的掩模未对准引起的耦合电容变化,并且数学证明了所有MPL分解的最坏情况耦合电容方案。我们的算法能够在最坏情况耦合电容上识别具有紧密绑定的分解,其可以用于定时/功率分析。我们的方法有望帮助工程师快速评估不同分解的质量,更好地了解MPL分解所带来的利弊。

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