基于NARX动态神经网络直拉硅单晶直径预测模型

O78%TM615%TP183; 直拉法在制备硅单晶的过程中存在机理假设多、多场耦合下边界条件不明确和化学变化交错且相互影响等问题,导致无法建立准确的机理模型用于硅单晶生长过程控制.针对此问题,本文以单晶炉拉晶车间的大量晶体生长数据为基础,基于互信息理论提出的最大信息系数(MIC)算法,对与晶体直径相关的特征参数进行分析,然后基于带外源输入的非线性自回归(NARX)动态神经网络,建立多输入单输出的等径阶段晶体直径预测模型,并对三台单晶炉拉晶数据进行直径预测,预测的平均均方误差值为0.000774.最后将NARX动态神经网络同反向传播(BP)神经网络进行对比分析,验证了该模型的优越性.结果表明,...

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Published in人工晶体学报 Vol. 51; no. 12; pp. 2031 - 2062
Main Authors 徐圣哲, 高德东, 王珊, 吴昊昊, 张西亚, 韩永龙, 李丽荣
Format Journal Article
LanguageChinese
Published 青海大学机械工程学院,西宁 810016%阳光能源(青海)有限公司,西宁 810007 01.12.2022
Subjects
数据驱动
非线性自回归
直拉法
硅单晶
BP神经网络
直径辨识
Online AccessGet full text
ISSN1000-985X
DOI10.3969/j.issn.1000-985X.2022.12.006

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Abstract O78%TM615%TP183; 直拉法在制备硅单晶的过程中存在机理假设多、多场耦合下边界条件不明确和化学变化交错且相互影响等问题,导致无法建立准确的机理模型用于硅单晶生长过程控制.针对此问题,本文以单晶炉拉晶车间的大量晶体生长数据为基础,基于互信息理论提出的最大信息系数(MIC)算法,对与晶体直径相关的特征参数进行分析,然后基于带外源输入的非线性自回归(NARX)动态神经网络,建立多输入单输出的等径阶段晶体直径预测模型,并对三台单晶炉拉晶数据进行直径预测,预测的平均均方误差值为0.000774.最后将NARX动态神经网络同反向传播(BP)神经网络进行对比分析,验证了该模型的优越性.结果表明,NARX动态神经网络为晶体直径的控制提供了一种更准确的辨识模型.
AbstractList O78%TM615%TP183; 直拉法在制备硅单晶的过程中存在机理假设多、多场耦合下边界条件不明确和化学变化交错且相互影响等问题,导致无法建立准确的机理模型用于硅单晶生长过程控制.针对此问题,本文以单晶炉拉晶车间的大量晶体生长数据为基础,基于互信息理论提出的最大信息系数(MIC)算法,对与晶体直径相关的特征参数进行分析,然后基于带外源输入的非线性自回归(NARX)动态神经网络,建立多输入单输出的等径阶段晶体直径预测模型,并对三台单晶炉拉晶数据进行直径预测,预测的平均均方误差值为0.000774.最后将NARX动态神经网络同反向传播(BP)神经网络进行对比分析,验证了该模型的优越性.结果表明,NARX动态神经网络为晶体直径的控制提供了一种更准确的辨识模型.
Author 王珊
李丽荣
高德东
吴昊昊
张西亚
韩永龙
徐圣哲
AuthorAffiliation 青海大学机械工程学院,西宁 810016%阳光能源(青海)有限公司,西宁 810007
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ZHANG Xiya
WU Haohao
WANG Shan
GAO Dedong
LI Lirong
XU Shengzhe
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Keywords 数据驱动
非线性自回归
直拉法
硅单晶
BP神经网络
直径辨识
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