A deep learning-based approach to material removal rate prediction in polishing

被引：135

作者：

Wang, Peng ^{[1
]}

Gao, Robert X. ^{[1
]}

Yan, Ruqiang ^{[1
]}

机构：

[1] Case Western Reserve Univ, Dept Mech & Aerosp Engn, Cleveland, OH 44106 USA

来源：

CIRP ANNALS-MANUFACTURING TECHNOLOGY | 2017年 / 66卷 / 01期

基金：

美国国家科学基金会;

关键词：

Polishing; Process control; Deep learning;

D O I：

10.1016/j.cirp.2017.04.013

中图分类号：

T [工业技术];

学科分类号：

120111 [工业工程];

摘要：

Prediction of material removal rate (MRR) during chemical mechanical polishing is critical for product quality control. Complexity involved in polishing makes it challenging to accurately predict MRR based on physical models. A data-driven technique based on Deep Belief Network (DBN) is investigated to reveal the relationship between MRR and polishing operation parameters such as pressure and rotational speeds of the wafer and pad. The effect of network structure and learning rate on the accuracy of predicted MRR is studied using particle swarm optimization algorithm. With an optimized network structure, the performance of DBN is experimentally verified, under varying operation conditions. (C) 2017 Published by Elsevier Ltd on behalf of CIRP.

引用

页码：429 / 432

页数：4

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