Advanced CMP Process Control by Using Machine Learning Image Analysis

Min-Hsuan Hsu, Chih-Chen Lin, Hsiang-Meng Yu, Kuang-Wei Chen, T. Luoh, Ling-Wu Yang, Tahone Yang, K. Chen
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引用次数: 1

Abstract

Chemical-mechanical polishing closed loop control optimized process with machine learning assisted wafer image analysis algorithm implemented on the inter layer dielectric of 3D NAND ON stacking with poly-silicon stop layer is studied. The grayscale wafer image can be responded for film residue, stop layer damage, wafer edge damage, and thickness variation. Polishing five zones control model is trainned with wafer grayscale value by Python NN model with two hidden layers. The best condition of closed loop feedback control is deduced by machine learning assisted wafer image analysis algorithm.
利用机器学习图像分析先进的CMP过程控制
研究了基于机器学习辅助晶圆图像分析算法的化学-机械抛光闭环控制优化过程,实现了具有多晶硅停止层的三维NAND on堆叠层间介质。灰度图像可以响应薄膜残留、停止层损伤、晶圆边缘损伤和厚度变化。采用两隐层Python神经网络模型,利用晶圆灰度值训练抛光五区控制模型。利用机器学习辅助晶圆图像分析算法,推导出闭环反馈控制的最佳条件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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