首页> 外文会议>Conference on alternative lithographic technologies VII >Defectivity prediction for droplet-dispensed UV nanoimprint lithography, enabled by fast simulation of resin flow at feature, droplet, and template scales
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Defectivity prediction for droplet-dispensed UV nanoimprint lithography, enabled by fast simulation of resin flow at feature, droplet, and template scales

机译:液滴分配的UV纳米压印光刻的缺陷预测,通过在特征,液滴和模板尺度上快速模拟树脂流动的快速模拟实现

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摘要

Full-field, physically-based simulation of nanoimprint lithography (NIL) is needed to address the throughput-versus-yield challenges that are currently faced by NIL. We demonstrate a simulation framework that can track the spreading and coalescence of tens of thousands of picoliter-volume resin droplets beneath a nanoimprint template, predicting evolution of feature filling and residual layer thickness (RLT) uniformity during the imprinting of geometrically complex designs such as found in solid-state memory. We have used the framework to explore directionality of droplet spreading beneath patterned templates, the role of template curvature in mitigating gas entrapment, and detrimental elastic deflections at wafer-edge partial imprint fields.
机译:全场,基于物理基于基于物理的纳米压印光刻(NIL)来解决当前面临的吞吐量与产生的挑战。我们展示了一种模拟框架,可以追踪纳米压印模板下方数万条皮瓣体积树脂液滴的扩展和聚结,预测在诸如发现的几何复杂设计的印记期间的特征填充和残余层厚度(RLT)均匀性的均匀性在固态记忆中。我们使用该框架探讨了图案化模板下面的液滴的方向性,模板曲率在减轻气体滞留中的作用,以及晶片边缘部分压印场的有害弹性偏转。

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