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机器学习应用于电子束刻法,以加速接触孔层的流程优化

Rongbo Zhao¹, Xiaolin Wang¹, Yayi Wei^2,3

¹Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China.

ACS applied materials & interfaces

|April 16, 2024

概括

这项研究将电子束光刻 (EBL) 实验与机器学习相结合,以优化芯片制造. 使用LSTM和SVM模型的新方法准确地预测了高分辨率图案设计的石版工艺条件.

科学领域:

材料科学材料科学材料科学
计算机科学计算机科学
电气工程电气工程

背景情况:

高分辨率的图案设计对于先进的芯片制造至关重要.
由于非线性系统动态,石版工艺的优化是复杂和耗时的.
目前的方法需要广泛的实验试验,在开发过程中造成瓶.

研究的目的:

开发一种高效的过程优化解决方案,用于金属氧化物纳米粒子光电阻的接触层.
利用机器学习来预测和分类最佳的光刻工艺条件.
为了加快半导体制造的光刻工艺的优化和控制.

主要方法:

电子束光刻 (EBL) 实验与机器学习算法的整合.
开发长期短期记忆 (LSTM) 网络,用于接触孔成像预测.
实现支持矢量机 (SVM) 模型用于过程条件分类.

主要成果:

该LSTM网络准确地预测了水平和垂直接触宽度,显示了与EBL实验数据的高度一致性.
结合的SVM和LSTM方法有效地选了符合关键维度统一性公差的工艺条件.
该分类模型在确定最佳过程参数方面表现出很好的准确性.

结论:

提出的基于机器学习的解决方案显著提高了石版工艺优化的效率.
这种方法为优化半导体制造中的接触层提供了可靠的参考.
将EBL与LSTM和SVM相结合,为克服芯片制造中的瓶提供了一个强大的策略.

关键词:

电子束 lithography 电子束 lithography 电子束 lithography 电子束 lithography 电子束 lithography 电子束长期短期内存网络长期内存网络机器学习是机器学习.过程优化优化过程优化支持矢量机器的支持矢量机器.

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