基于ISOMAP-DE-SVM的Cz单晶硅等径阶段掉苞预测
[1] 董建明,张 波,刘 进,等.直拉法硅单晶生长中断棱与掉苞问题的探讨[J].材料导报,2013,27(S1):157-159.
[2] 苏文佳,李九龙,杨 伟,等.直拉法单晶硅中位错影响因素研究进展[J].硅酸盐学报,2021,49(4):723-735.
[3] 裴志军,纪秀峰,刘 峰.4英寸<111>硅单晶制备中的“断棱”与“掉苞”问题[J].半导体杂志,1998(3):20-23.
[4] SATUNKIN G A. Mathematical modelling and control system design of Czochralski and liquid encapsulated Czochralski processes: the basic low order mathematical model[J]. Journal of Crystal Growth, 1995, 154(1/2): 172-188.
[5] ARMAOU A, CHRISTOFIDES P D. Crystal temperature control in the Czochralski crystal growth process[J]. AIChE Journal, 2001, 47(1): 79-106.
[6] LIU D, LIANG J L. A Bayesian approach to diameter estimation in the diameter control system of silicon single crystal growth[J]. IEEE Transactions on Instrumentation and Measurement, 2011, 60(4): 1307-1315.
[7] 黄伟超.多场作用下Cz法晶体生长过程建模与数值模拟[D].西安:西安理工大学,2018.
[8] KATO S, KIM S, KANO M, et al. Gray-box modeling of 300 mm diameter Czochralski single-crystal Si production process[J]. Journal of Crystal Growth, 2021, 553: 125929.
[9] REN J C, LIU D, WAN Y. Modeling and application of Czochralski silicon single crystal growth process using hybrid model of data-driven and mechanism-based methodologies[J]. Journal of Process Control, 2021, 104: 74-85.
[10] 肖立志.机器学习数据驱动与机理模型融合及可解释性问题[J].石油物探,2022,61(2):205-212.
[11] 杜佳晨.基于数据挖掘的单晶硅等径生长过程“掉苞”预测方法研究[D].杭州:浙江大学,2019.
[12] ZHANG J, LIU H, CAO J W, et al. A deep learning based dislocation detection method for cylindrical crystal growth process[J]. Applied Sciences, 2020, 10(21): 7799.
[13] 赵华东,翟晓彤,田增国,等.基于MIC的Cz单晶硅放肩阶段关键特征参数辨识[J].人工晶体学报,2020,49(4):607-612.
[14] 李欣鸽.CZ硅单晶等径生长阶段关键参数优化研究[D].郑州:郑州大学,2020.
[15] 黄 亮,彭 清,谢长君,等.基于差分进化优化的支持向量机燃料电池故障诊断[J].电源技术,2021,45(10):1316-1319.
[16] 刘 鑫,韩 强,周永帅,等.基于GA优化SVM参数的白酒分类识别方法应用研究[J].包装与食品机械,2022,40(2):64-68.
[17] 吴贵军,范鹏生,陈浩辰,等.基于深度学习的数据分类预测及应用[J].无线互联科技,2022,19(8):126-127.
[18] 高旭旭.基于深度学习的分类预测算法研究及实现[D].北京:北京邮电大学,2019.
[19] XIAO C W, YE J Q, ESTEVES R M, et al. Using Spearman’s correlation coefficients for exploratory data analysis on big dataset[J]. Concurrency and Computation: Practice and Experience, 2016, 28(14): 3866-3878.
[20] RESHEF D N, RESHEF Y A, FINUCANE H K, et al. Detecting novel associations in large data sets[J]. Science, 2011, 334(6062): 1518-1524.
[21] 周志华.机器学习[M].北京:清华大学出版社,2016.
[22] PRICE K V, STRON R M, LAMPINEN J. Differential evolution: a practical approach to global optimization[M]. Springer Science & Business Media, 2005.
[23] HANCER E, XUE B, ZHANG M J. Differential evolution for filter feature selection based on information theory and feature ranking[J]. Knowledge-Based Systems, 2018, 140: 103-119.
[24] ABUROMMAN A A, IBNE REAZ M B. A novel weighted support vector machines multiclass classifier based on differential evolution for intrusion detection systems[J]. Information Sciences, 2017, 414: 225-246.
[25] 牛 岩,魏雨露,刘思琪.数据标准化方法对SVM数据预测结果的影响研究[J].土地开发工程研究,2020,5(4):18-23.
[26] ANOWAR F, SADAOUI S, SELIM B. Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE)[J]. Computer Science Review, 2021, 40: 100378.
侯少华, 张宏帅, 姜宝柱, 朱宾宾, 田增国. 基于ISOMAP-DE-SVM的Cz单晶硅等径阶段掉苞预测[J]. 人工晶体学报, 2023, 52(1): 25. HOU Shaohua, ZHANG Hongshuai, JIANG Baozhu, ZHU Binbin, TIAN Zengguo. Broken Edge Prediction in the Equal-Diameter Growth Process of Cz Single Crystal Silicon Based on ISOMAP-DE-SVM[J]. Journal of Synthetic Crystals, 2023, 52(1): 25.