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首页> 外文期刊>ACS Macro Letters >Evolutionary Optimization of Directed Self-Assembly of Triblock Copolymers on Chemically Patterned Substrates
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Evolutionary Optimization of Directed Self-Assembly of Triblock Copolymers on Chemically Patterned Substrates

机译：化学构图基材上三嵌段共聚物定向自组装的进化优化

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摘要

Directed self-assembly of block copolymers on chemical patterns is of considerable interest for sublithographic patterning. The concept of pattern interpolation, in which a subset of features patterned on a substrate is multiplied through the inherent morphology of an ordered block copolymer, has enabled fabrication of extremely small, defect-free features over large areas. One of the central challenges in design of pattern interpolation strategies is that of identifying system characteristics leading to ideal, defect-free directed assembly. In this work we demonstrate how a coarse-grained many-body model of block copolymers, coupled to an evolutionary computation (EC) strategy, can be used to design and optimize substrate-copolymer combinations for use in lithographic patterning. The proposed approach is shown to be significantly more effective than traditional algorithms based on random searches, and its results are validated in the context of recent experimental observations. The coupled simulation-evolution method introduced here provides a general and efficient method for potential design of complex device-oriented structures.
机译：嵌段共聚物在化学图案上的直接自组装对于亚光刻图案化非常重要。图案插值的概念（通过在基片上构图的特征子集通过有序嵌段共聚物的固有形态倍增）得以实现，从而可以在大面积上制造极小的无缺陷特征。模式插值策略设计的主要挑战之一是识别导致理想，无缺陷的定向装配的系统特性。在这项工作中，我们演示了如何将嵌段共聚物的粗粒多体模型与演化计算（EC）策略结合使用，来设计和优化用于光刻图案的底物-共聚物组合。所提出的方法显示出比基于随机搜索的传统算法明显更有效，并且其结果在最近的实验观察中得到了验证。此处介绍的耦合仿真-进化方法为复杂的面向设备的结构的潜在设计提供了一种通用而有效的方法。

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