Template Design for Complex Block Copolymer Patterns Using a Machine Learning Method

Zhihan Liu

doi:10.1021/acsami.3c05018

2023-06-19

PRE

DOI：10.1021/acsami.3c05018

原文链接

摘要

En 中文

This study represents the first attempt to address theinversedesign problem of the guiding template for directed self-assembly(DSA) patterns using solely machine learning methods. By formulatingthe problem as a multi-label classification task, the study showsthat it is possible to predict templates without requiring any forwardsimulations. A series of neural network (NN) models, ranging fromthe basic two-layer convolutional neural network (CNN) to the largeNN models (32-layer CNN with 8 residual blocks), have been trainedusing simulated pattern samples generated by thousands of self-consistentfield theory (SCFT) calculations; a number of augmentation techniques,especially suitable for predicting morphologies, have been also proposedto enhance the performance of the NN model. The exact match accuracyof the model in predicting the template of simulated patterns wassignificantly improved from 59.8% for the baseline model to 97.1%for the best model of this study. The best model also demonstratesan excellent generalization ability in predicting the template forhuman-designed DSA patterns, while the simplest baseline model isineffective in this task.

Keyword:

block copolymer

directed self-assembly

machinelearning

inverse design

lithography

期刊

ACS Applied Materials and Interfaces

IF:

8.2

论文数:

6.1W

被引数:

38.7W

机构

fudan university

学者数:

7.0K

论文数: 1.8K

被引数: 0

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