摘要
为了在大数据环境下利用制造数据对半导体加工周期进行准确预测,针对传统预测模型准确性和泛性上的不足提出一种多层数据分析框架,基于该框架实现加工周期预测算法,利用某半导体生产线数据建立预测模型,检验了该预测方法的有效性并与多种常用方法进行了比较。结果表明,基于多层数据分析框架的半导体加工周期预测方法有效提高了模型的准确性和泛性。
To predict cycle time accurately of semiconductor processing based on manufacturing data and to cover the shortage of traditional model s generalization, a multi-layer data analysis framework was proposed. A cycle time prediction algorithm based on this framework was realized. Based on the data of a semiconductor line, a prediction model was built, and the effectiveness of this method was validated by comparing with several common methods. Experiments demonstrated that the proposed multi-layer data analysis framework based cycle time prediction method could effectively improve the accuracy and the generalization.
作者
汤珺雅
李莉
TANG Junya;LI Li(School of Electronics and Information Engineering,Tongji University,Shanghai 201804,China)
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2019年第5期1086-1092,共7页
Computer Integrated Manufacturing Systems
基金
国家自然科学基金资助项目(51475334)~~
关键词
加工周期预测
半导体制造
多层数据分析框架
机器学习
集成学习
cycle time prediction
semiconductor manufacturing
multi-layer data analysis framework
machine learning
ensemble learning