Machine Learning Predictions of Block Copolymer Self‐Assembly,Advanced Materials

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Machine Learning Predictions of Block Copolymer Self‐Assembly
Advanced Materials ( IF 27.4 ) Pub Date : 2020-11-18 , DOI: 10.1002/adma.202005713
Kun‐Hua Tu ₁ , Hejin Huang ₁ , Sangho Lee ₁ , Wonmoo Lee ₁ , Zehao Sun ₁ , Alfredo Alexander‐Katz ₁ , Caroline A. Ross ₁

Affiliation

Directed self‐assembly of block copolymers is a key enabler for nanofabrication of devices with sub‐10 nm feature sizes, allowing patterning far below the resolution limit of conventional photolithography. Among all the process steps involved in block copolymer self‐assembly, solvent annealing plays a dominant role in determining the film morphology and pattern quality, yet the interplay of the multiple parameters during solvent annealing, including the initial thickness, swelling, time, and solvent ratio, makes it difficult to predict and control the resultant self‐assembled pattern. Here, machine learning tools are applied to analyze the solvent annealing process and predict the effect of process parameters on morphology and defectivity. Two neural networks are constructed and trained, yielding accurate prediction of the final morphology in agreement with experimental data. A ridge regression model is constructed to identify the critical parameters that determine the quality of line/space patterns. These results illustrate the potential of machine learning to inform nanomanufacturing processes.

中文翻译：

嵌段共聚物自组装的机器学习预测

嵌段共聚物的直接自组装是特征尺寸小于10 nm的器件纳米加工的关键因素，可以进行远远低于常规光刻技术分辨率的构图。在嵌段共聚物自组装涉及的所有工艺步骤中，溶剂退火在决定膜的形态和图案质量方面起着主导作用，而溶剂退火过程中多个参数的相互作用，包括初始厚度，溶胀，时间和溶剂比率导致难以预测和控制最终的自组装模式。在这里，机器学习工具被应用于分析溶剂退火过程并预测过程参数对形态和缺陷率的影响。构建和训练了两个神经网络，与实验数据一致，可以准确预测最终的形态。构建岭回归模型以识别确定线/空间图案质量的关键参数。这些结果说明了机器学习为纳米制造过程提供信息的潜力。

更新日期：2020-12-28

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