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:通过在特征,液滴和模板比例下快速模拟树脂流动,可以预测液滴分布的UV纳米压印光刻的缺陷率

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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:需要对纳米压印光刻技术(NIL)进行基于物理的全场模拟,以解决NIL当前面临的产量与产量之间的挑战。我们演示了一个模拟框架,该框架可以跟踪纳米压印模板下成千上万微微升体积的树脂滴的扩散和聚结,预测在压印诸如几何图形之类的复杂设计过程中特征填充和残余层厚度(RLT)均匀性的演变在固态存储器中。我们已经使用该框架来探索液滴在图案化模板下方散布的方向性,模板曲率在缓解气体滞留方面的作用以及晶片边缘部分压印场的有害弹性挠度。

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