搜索

x

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于前馈神经网络的等离子体光谱诊断方法

王彦飞 朱悉铭 张明志 孟圣峰 贾军伟 柴昊 王旸 宁中喜

引用本文:
Citation:

基于前馈神经网络的等离子体光谱诊断方法

王彦飞, 朱悉铭, 张明志, 孟圣峰, 贾军伟, 柴昊, 王旸, 宁中喜

Plasma optical emission spectroscopy based on feedforward neural network

Wang Yan-Fei, Zhu Xi-Ming, Zhang Ming-Zhi, Meng Sheng-Feng, Jia Jun-Wei, Chai Hao, Wang Yang, Ning Zhong-Xi
PDF
HTML
导出引用
  • 光谱诊断在等离子体刻蚀、材料处理、等离子体设备和工艺开发, 以及航天等离子体推进等领域得到了广泛的应用. 光谱诊断依赖的碰撞辐射模型会受到碰撞截面等基础物理数据所含偏差的影响, 导致诊断结果出现误差. 针对这一问题, 本文开发了一种基于前馈神经网络的等离子体光谱解算方法. 通过对比新方法与以往常用的最小二乘诊断方法的误差特性, 发现神经网络诊断方法能够通过辨识光谱向量的主要特征, 减小基础数据偏差向诊断结果的传递. 对实验光谱数据的分析进一步印证了这一点. 本文还对神经网络算法对抗基础数据偏差的机理进行了分析. 这种方法在等离子体参数在线监测、成像监测海量数据处理等领域具有良好的应用前景.
    Optical emission spectroscopy (OES) has been widely applied to plasma etching, material processing, development of plasma equipment and technology, as well as plasma propulsion. The collisional-radiative model used in OES is affected by the deviation of fundamental data such as collision cross sections, thus leading to the error in diagnostic results. In this work, a novel method is developed based on feedforward neural network for OES. By comparing the error characteristics of the new method with those of the traditional least-square diagnostic method, it is found that the neural network diagnosis method can reduce the transmission of basic data deviation to the diagnosis results by identifying the characteristics of the spectral vector. This is confirmed by the experimental results. Finally, the mechanism of the neural network algorithm against fundamental data deviation is analyzed. This method also has a good application prospect in plasma parameter online monitoring, imaging monitoring and mass data processing.
      通信作者: 朱悉铭, zhuximing@hit.edu.cn ; 宁中喜, ningzx@hit.edu.cn
    • 基金项目: 国家自然科学基金(批准号: 11775063)、国防基础科研计划(批准号: JCKY2018203B029)和国防计量基础课题(批准号: JSJL2016203B017)资助的课题
      Corresponding author: Zhu Xi-Ming, zhuximing@hit.edu.cn ; Ning Zhong-Xi, ningzx@hit.edu.cn
    • Funds: Project supported by the National Natural Science Foundation of China (Grant No. 11775063), the Defense Industrial Technology Development Program, China (Grant No. JCKY2018203B029), and the Defense Industrial Metering Program, China (Grant No. JSJL2016203B017)
    [1]

    Donnelly, Vincent M, Avinoam K 2013 J. Vac. Sci. Technol., A 31 050825Google Scholar

    [2]

    曲鹏程, 唐代飞, 向鹏飞, 袁安波 2017 电子科技 30 153Google Scholar

    Qu P C, Tang D F, Xiang P F, Yuan A B 2017 Electr. Sci. Technol. 30 153Google Scholar

    [3]

    Edy R, Huang G S, Zhao Y T, Guo Y, Zhang J, Mei Y F, Shi J J 2017 Surf. Coat. Technol. 329 149Google Scholar

    [4]

    王巍, 叶甜春, 李兵, 陈大鹏, 刘明 2005 半导体技术 30 13Google Scholar

    Wang W, Ye T C, Li B, Chen D P, Liu M 2005 Semiconductor Technol. 30 13Google Scholar

    [5]

    王巍, 王玉青, 孙江宏, 兰中文, 龚云贵 2008 红外与激光工程 4 748Google Scholar

    Wang W, Wang Y Q, Sun J H, Lan Z W, Gong Y G 2008 Infrared Laser Eng. 4 748Google Scholar

    [6]

    Sridhar S, Donnelly V M, Liu L, Economou D J 2016 J. Vac. Sci. Technol., A 34 061303Google Scholar

    [7]

    Gao J, Zhou L, Liang J, Wang Z, Wu Y, Muhammad J, Dong X, Li S, Yu H, Quan X 2018 Nano Res. 11 1470Google Scholar

    [8]

    Kyung K, Winderbaum S, Hameiri Z 2017 Surf. Coat. Technol. 328 204Google Scholar

    [9]

    Yang J, Yokota S, Kaneko R, Komurasaki K 2010 Phys. Plasmas 17 103504Google Scholar

    [10]

    Zhu X M, Wang Y F, Wang Y, Yu D R, Zatsarinny O, Bartschat K, Tsankov T V, Czarnetzki U 2019 Plasma Sources Sci. Technol. 28 105005Google Scholar

    [11]

    Donnelly V M 2004 J. Phys. D: Appl. Phys. 3 7

    [12]

    Stafford L, Khare R, Donnelly V M, Margot J, Moisan M 2009 Appl. Phys. Lett. 94 021503Google Scholar

    [13]

    Wang Q, Koleva I, Donnelly V M, Economou D J 2005 J. Phys. D: Appl. Phys. 38 1690Google Scholar

    [14]

    Huang X J, Zhang J, Guo Y, Zhang J, Shi J J 2014 IEEE Trans. Plasma Sci. 42 3569Google Scholar

    [15]

    孙殿平 2008 博士学位论文 (上海: 华东师范大学)

    Sun D P 2008 Ph. D. Dissertation (Shanghai: East China Normal University) (in Chinese)

    [16]

    刘冲, 何湘, 朱卫华 2016 光谱学与光谱分析 S1 469

    Liu C, He X, Zhu W H 2016 Spectrosc. Spect. Anal. S1 469

    [17]

    Zhu X M, Pu Y K 2009 J. Phys. D: Appl. Phys. 43 015204

    [18]

    Zhu X M, Pu Y K 2010 J Phys. D: Appl. Phys. 43 403001Google Scholar

    [19]

    Zhu X M, Chen W C, Li J, Cheng Z W, Pu Y K 2012 Plasma Sources Sci. Technol. 21 045009Google Scholar

    [20]

    Boffard J B, Lin C C, DeJoseph C A 2004 J. Phys. D: Appl. Phys. 37 R 37 R143Google Scholar

    [21]

    Sadeghi N, Setser D W 2001 J. Chem. Phys. 115 3144

    [22]

    Weber T, Boffard J B, Lin C C 2003 Phys. Rev. A 68 032719Google Scholar

    [23]

    Sharma L, Srivastava R, Stauffer A D 2011 Eur. Phys. J. D 62 399Google Scholar

    [24]

    Zatsarinny O, Bartschat K 2013 J. Phys. B: At. Mol. Opt. 46 112001Google Scholar

    [25]

    Bray I, Fursa D, Kadyrov A, Stelbovicsa A T, Kheifets A S, Mukhamedzhanov A M 2012 Phys. Rep. 520 135Google Scholar

    [26]

    Chen Z B, Dong C Z, Xie L Y, Jiang J 2014 Chin. Phys. Lett. 31 033401Google Scholar

    [27]

    Boffard J B, Jung R O, Lin C C, Wendt A E 2009 Plasma Sources Sci. Technol. 18 035017Google Scholar

    [28]

    Boffard J B, Jung R O, Lin C C, Wendt A E 2010 Plasma Sources Sci. Technol. 19 065001Google Scholar

    [29]

    Terzi M, Masiero C, Beghi A, Maggipinto M, Susto G A 2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry Modena, Italy, September 11−13, 2017 p17244916

    [30]

    Wang C Y, Hsu C C 2019 Plasma Sources Sci. Technol. 28 105013Google Scholar

    [31]

    康志伟, 刘拓, 刘劲, 马辛, 陈晓 2020 物理学报 69 069701Google Scholar

    Kang Z W, Liu T, Liu J, Ma X, Chen X 2020 Acta Phys. Sin. 69 069701Google Scholar

    [32]

    丁刚, 钟诗胜 2007 物理学报 2 1224Google Scholar

    Ding G, Zhong S S 2007 Acta Phys. Sin. 2 1224Google Scholar

    [33]

    徐启伟, 王佩佩, 曾镇佳, 黄泽斌, 周新星, 刘俊敏, 李瑛, 陈书青, 范滇元 2020 物理学报 69 014209Google Scholar

    Xu Q W, Wang P P, Zeng Z J, Huang Z B, Zhou X X, Liu J M, Li Y, Chen S Q, Fan D Y 2020 Acta Phys. Sin. 69 014209Google Scholar

    [34]

    彭向凯, 吉经纬, 李琳, 任伟, 项静峰, 刘亢亢, 程鹤楠, 张镇, 屈求智, 李唐, 刘亮, 吕德胜 2019 物理学报 68 130701Google Scholar

    Peng X K, Ji J W, Li L, Ren W, Xiang J F, Liu K K, Cheng H N, Zhang Z, Qu Q Z, Li T, Liu L, Lv D S 2019 Acta Phys. Sin. 68 130701Google Scholar

    [35]

    王鹏举, 范俊宇, 苏艳, 赵纪军 2020 物理学报 69 238702

    Wang P J, Fan J Y, Su Y, Zhao J J 2020 Acta Phys. Sin. 69 238702

    [36]

    彭相洲, 陈雨 2020 计算机应用研究 37 47

    Peng X Z, Chen Y 2020 Appl. Res. Com. 37 47 (in Chinese)

    [37]

    孟圣峰 2019 硕士学位论文 (哈尔滨: 哈尔滨工业大学)

    Meng S F 2019 M. S. Thesis (Harbin: Harbin Institute of Technology)(in Chinese)

    [38]

    Zhu X M, Pu Y K 2008 Plasma Sources Sci. Technol. 17 024002Google Scholar

    [39]

    Abdollah S, Nikiforov A Y, Leys C 2010 Phys. Plasmas 17 063504Google Scholar

    [40]

    Zhu X M, Chen W C, Li J, Pu Y K 2008 J. Phys. D: Appl. Phys. 42 025203

  • 图 1  考夫曼电离室结构及测量实验方案

    Fig. 1.  Structure of the Kaufmann discharge chamber and the scheme of measurement.

    图 2  基于最小二乘的光谱诊断方法流程

    Fig. 2.  Diagram of optical emission spectroscopy based on least square method.

    图 3  基于前馈神经网络的光谱诊断方法流程

    Fig. 3.  Diagram of optical emission spectroscopy based on feedforward neural network.

    图 4  误差半径及偏心距定义(真实值)

    Fig. 4.  Definition of error radius and eccentricity.

    图 5  使用不同网络结构和数据正规化方法获得的均方误差随迭代次数的变化

    Fig. 5.  Variation of mean square error with the number of iterations using different network structures and data normalization methods.

    图 6  网络预测结果与训练目标的对应关系 (a)电子温度的对应关系; (b)电子密度的对应关系; (c)电子温度的预测误差; (d)电子密度的预测误差

    Fig. 6.  Corresponding relationship between the network prediction result and the training target: (a) Corresponding relationship of the electron temperature; (b) corresponding relationship of the electron density; (c) prediction error of the electron temperature; (d) prediction error of the electron density.

    图 7  使用最小二乘方法获得的拟合结果(为保证图的可读性, 对离子谱线强度进行了放大处理, 并将拟合所得光谱的波长进行了偏置)

    Fig. 7.  Fitting results obtained by the least square method (in order to improve the readability of the image, the intensity of the ion spectral line is amplified, and a bias is introduced into the wavelength of the fitting spectrum).

    图 8  (a)最小二乘方法诊断结果的平均误差半径; (b) 神经网络方法诊断结果的平均误差半径

    Fig. 8.  (a) Average error radius of the diagnosis result of the least square method; (b) average error radius of the diagnosis result of the neural network method.

    图 9  (a)最小二乘方法诊断结果的最大误差半径; (b) 神经网络方法诊断结果的最大误差半径

    Fig. 9.  (a) The maximum error radius of the diagnosis result of the least square method; (b) the maximum error radius of the diagnosis result of the neural network method.

    图 10  (a)最小二乘方法结果的偏心距; (b) 神经网络方法结果的偏心距

    Fig. 10.  (a) Eccentricity of the diagnosis result of the least square method; (b) the eccentricity of the diagnosis result of the neural network method.

    图 11  (a)考夫曼离子源中电子密度的诊断结果; (b)考夫曼离子源中电子温度的诊断结果; (c)最小二乘方法和神经网络方法获得的电子密度结果的相对误差; (d)最小二乘方法和神经网络方法获得的电子温度结果的相对误差. “探针”、“最小二乘”和“神经网络”分别表示由朗缪尔探针、最小二乘方法和神经网络方法获得的诊断结果

    Fig. 11.  (a) Diagnostic results of ne in Kaufman ion source; (b) diagnostic results of Te in Kaufman ion source; (c) relative error of ne by least-square method and neural network method; (d) relative error of Te by least-square method and neural network method. “探针”, “最小二乘”, “神经网络” denotes the diagnostic results obtained by Langmuir probe, least-square diagnostic method and neural network diagnostic method, respectively.

    表 1  本文研究中选用的氙谱线表

    Table 1.  Xenon spectral lines used in this work.

    序号波长/nm上能级序号波长/nm上能级
    1460.3035p4(3P2)6p $ {}^{2}{\left[1\right]}_{3/2}^\circ $9834.7455p5($ {}^{2}{\mathrm{P}}_{1/2}^\circ $)6p 2[3/2]2
    2484.4335p4(3P2)6p $ {}^{2}{\left[3\right]}_{7/2}^\circ $10840.9195p5($ {}^{2}{\mathrm{P}}_{3/2}^\circ $)6p 2[3/2]1
    3492.1485p4(3P1)6p $ {}^{2}{\left[2\right]}_{5/2}^\circ $11881.9415p5($ {}^{2}{\mathrm{P}}_{3/2}^\circ $)6p 2[5/2]3
    4529.2225p4(3P2)6p $ {}^{2}{\left[2\right]}_{5/2}^\circ $12895.2255p5($ {}^{2}{\mathrm{P}}_{3/2}^\circ $)6p 2[3/2]2
    5541.9155p4(3P2)6p $ {}^{2}{\left[3\right]}_{5/2}^\circ $13904.5455p5($ {}^{2}{\mathrm{P}}_{3/2}^\circ $)6p 2[5/2]2
    6788.7395p5(21/2)6p 2[1/2]014916.2655p5($ {}^{2}{\mathrm{P}}_{3/2}^\circ $)6p 2[3/2]1
    7823.1635p5(23/2)6p 2[3/2]215979.9705p5($ {}^{2}{\mathrm{P}}_{3/2}^\circ $)6p 2[1/2]1
    8828.0125p5(23/2)6p 2[1/2]016992.3205p5($ {}^{2}{\mathrm{P}}_{3/2}^\circ $)6p 2[5/2]2
    下载: 导出CSV
  • [1]

    Donnelly, Vincent M, Avinoam K 2013 J. Vac. Sci. Technol., A 31 050825Google Scholar

    [2]

    曲鹏程, 唐代飞, 向鹏飞, 袁安波 2017 电子科技 30 153Google Scholar

    Qu P C, Tang D F, Xiang P F, Yuan A B 2017 Electr. Sci. Technol. 30 153Google Scholar

    [3]

    Edy R, Huang G S, Zhao Y T, Guo Y, Zhang J, Mei Y F, Shi J J 2017 Surf. Coat. Technol. 329 149Google Scholar

    [4]

    王巍, 叶甜春, 李兵, 陈大鹏, 刘明 2005 半导体技术 30 13Google Scholar

    Wang W, Ye T C, Li B, Chen D P, Liu M 2005 Semiconductor Technol. 30 13Google Scholar

    [5]

    王巍, 王玉青, 孙江宏, 兰中文, 龚云贵 2008 红外与激光工程 4 748Google Scholar

    Wang W, Wang Y Q, Sun J H, Lan Z W, Gong Y G 2008 Infrared Laser Eng. 4 748Google Scholar

    [6]

    Sridhar S, Donnelly V M, Liu L, Economou D J 2016 J. Vac. Sci. Technol., A 34 061303Google Scholar

    [7]

    Gao J, Zhou L, Liang J, Wang Z, Wu Y, Muhammad J, Dong X, Li S, Yu H, Quan X 2018 Nano Res. 11 1470Google Scholar

    [8]

    Kyung K, Winderbaum S, Hameiri Z 2017 Surf. Coat. Technol. 328 204Google Scholar

    [9]

    Yang J, Yokota S, Kaneko R, Komurasaki K 2010 Phys. Plasmas 17 103504Google Scholar

    [10]

    Zhu X M, Wang Y F, Wang Y, Yu D R, Zatsarinny O, Bartschat K, Tsankov T V, Czarnetzki U 2019 Plasma Sources Sci. Technol. 28 105005Google Scholar

    [11]

    Donnelly V M 2004 J. Phys. D: Appl. Phys. 3 7

    [12]

    Stafford L, Khare R, Donnelly V M, Margot J, Moisan M 2009 Appl. Phys. Lett. 94 021503Google Scholar

    [13]

    Wang Q, Koleva I, Donnelly V M, Economou D J 2005 J. Phys. D: Appl. Phys. 38 1690Google Scholar

    [14]

    Huang X J, Zhang J, Guo Y, Zhang J, Shi J J 2014 IEEE Trans. Plasma Sci. 42 3569Google Scholar

    [15]

    孙殿平 2008 博士学位论文 (上海: 华东师范大学)

    Sun D P 2008 Ph. D. Dissertation (Shanghai: East China Normal University) (in Chinese)

    [16]

    刘冲, 何湘, 朱卫华 2016 光谱学与光谱分析 S1 469

    Liu C, He X, Zhu W H 2016 Spectrosc. Spect. Anal. S1 469

    [17]

    Zhu X M, Pu Y K 2009 J. Phys. D: Appl. Phys. 43 015204

    [18]

    Zhu X M, Pu Y K 2010 J Phys. D: Appl. Phys. 43 403001Google Scholar

    [19]

    Zhu X M, Chen W C, Li J, Cheng Z W, Pu Y K 2012 Plasma Sources Sci. Technol. 21 045009Google Scholar

    [20]

    Boffard J B, Lin C C, DeJoseph C A 2004 J. Phys. D: Appl. Phys. 37 R 37 R143Google Scholar

    [21]

    Sadeghi N, Setser D W 2001 J. Chem. Phys. 115 3144

    [22]

    Weber T, Boffard J B, Lin C C 2003 Phys. Rev. A 68 032719Google Scholar

    [23]

    Sharma L, Srivastava R, Stauffer A D 2011 Eur. Phys. J. D 62 399Google Scholar

    [24]

    Zatsarinny O, Bartschat K 2013 J. Phys. B: At. Mol. Opt. 46 112001Google Scholar

    [25]

    Bray I, Fursa D, Kadyrov A, Stelbovicsa A T, Kheifets A S, Mukhamedzhanov A M 2012 Phys. Rep. 520 135Google Scholar

    [26]

    Chen Z B, Dong C Z, Xie L Y, Jiang J 2014 Chin. Phys. Lett. 31 033401Google Scholar

    [27]

    Boffard J B, Jung R O, Lin C C, Wendt A E 2009 Plasma Sources Sci. Technol. 18 035017Google Scholar

    [28]

    Boffard J B, Jung R O, Lin C C, Wendt A E 2010 Plasma Sources Sci. Technol. 19 065001Google Scholar

    [29]

    Terzi M, Masiero C, Beghi A, Maggipinto M, Susto G A 2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry Modena, Italy, September 11−13, 2017 p17244916

    [30]

    Wang C Y, Hsu C C 2019 Plasma Sources Sci. Technol. 28 105013Google Scholar

    [31]

    康志伟, 刘拓, 刘劲, 马辛, 陈晓 2020 物理学报 69 069701Google Scholar

    Kang Z W, Liu T, Liu J, Ma X, Chen X 2020 Acta Phys. Sin. 69 069701Google Scholar

    [32]

    丁刚, 钟诗胜 2007 物理学报 2 1224Google Scholar

    Ding G, Zhong S S 2007 Acta Phys. Sin. 2 1224Google Scholar

    [33]

    徐启伟, 王佩佩, 曾镇佳, 黄泽斌, 周新星, 刘俊敏, 李瑛, 陈书青, 范滇元 2020 物理学报 69 014209Google Scholar

    Xu Q W, Wang P P, Zeng Z J, Huang Z B, Zhou X X, Liu J M, Li Y, Chen S Q, Fan D Y 2020 Acta Phys. Sin. 69 014209Google Scholar

    [34]

    彭向凯, 吉经纬, 李琳, 任伟, 项静峰, 刘亢亢, 程鹤楠, 张镇, 屈求智, 李唐, 刘亮, 吕德胜 2019 物理学报 68 130701Google Scholar

    Peng X K, Ji J W, Li L, Ren W, Xiang J F, Liu K K, Cheng H N, Zhang Z, Qu Q Z, Li T, Liu L, Lv D S 2019 Acta Phys. Sin. 68 130701Google Scholar

    [35]

    王鹏举, 范俊宇, 苏艳, 赵纪军 2020 物理学报 69 238702

    Wang P J, Fan J Y, Su Y, Zhao J J 2020 Acta Phys. Sin. 69 238702

    [36]

    彭相洲, 陈雨 2020 计算机应用研究 37 47

    Peng X Z, Chen Y 2020 Appl. Res. Com. 37 47 (in Chinese)

    [37]

    孟圣峰 2019 硕士学位论文 (哈尔滨: 哈尔滨工业大学)

    Meng S F 2019 M. S. Thesis (Harbin: Harbin Institute of Technology)(in Chinese)

    [38]

    Zhu X M, Pu Y K 2008 Plasma Sources Sci. Technol. 17 024002Google Scholar

    [39]

    Abdollah S, Nikiforov A Y, Leys C 2010 Phys. Plasmas 17 063504Google Scholar

    [40]

    Zhu X M, Chen W C, Li J, Pu Y K 2008 J. Phys. D: Appl. Phys. 42 025203

  • [1] 陈存宇, 陈爱喜, 戚晓秋, 王韩奎. 基于MLP神经网络优化改进的BW模型. 物理学报, 2025, 74(1): 1-11. doi: 10.7498/aps.74.20241201
    [2] 方泽, 潘泳全, 戴栋, 张俊勃. 基于源项解耦的物理信息神经网络方法及其在放电等离子体模拟中的应用. 物理学报, 2024, 73(14): 145201. doi: 10.7498/aps.73.20240343
    [3] 王均武, 玄洪文, 俞航航, 王新兵, Vassily S. Zakharov. 激光诱导放电等离子体极紫外辐射的模拟. 物理学报, 2024, 73(1): 015203. doi: 10.7498/aps.73.20231158
    [4] 谢卓, 温智琳, 司明奇, 窦银萍, 宋晓伟, 林景全. 双激光脉冲打靶形成Gd等离子体的极紫外光谱辐射. 物理学报, 2022, 71(3): 035202. doi: 10.7498/aps.71.20211450
    [5] 孟举, 何贞岑, 颜君, 吴泽清, 姚科, 李冀光, 吴勇, 王建国. 电四极跃迁对电子束离子阱等离子体中离子能级布居的影响. 物理学报, 2022, 71(19): 195201. doi: 10.7498/aps.71.20220489
    [6] 谢卓, 温志琳, 司明奇, 窦银萍, 宋晓伟, 林景全. 双激光脉冲打靶形成Gd等离子体的极紫外光谱辐射研究. 物理学报, 2021, (): . doi: 10.7498/aps.70.20211450
    [7] 韩小英, 李凌霄, 戴振生, 郑无敌, 古培俊, 吴泽清. 一个快速模拟热稠密非平衡等离子体的碰撞辐射模型. 物理学报, 2021, 70(11): 115202. doi: 10.7498/aps.70.20201946
    [8] 章太阳, 陈冉. 东方超环(EAST)装置中等离子体边界锂杂质的碰撞-辐射模型. 物理学报, 2017, 66(12): 125201. doi: 10.7498/aps.66.125201
    [9] 吴坚, 李兴文, 李沫, 杨泽锋, 史宗谦, 贾申利, 邱爱慈. AlK壳层等离子体辐射谱模型的比对. 物理学报, 2015, 64(20): 205201. doi: 10.7498/aps.64.205201
    [10] 谢会乔, 谭熠, 刘阳青, 王文浩, 高喆. 中国联合球形托卡马克氦放电等离子体的碰撞辐射模型及其在谱线比法诊断的应用. 物理学报, 2014, 63(12): 125203. doi: 10.7498/aps.63.125203
    [11] 郭凯敏, 高 勋, 郝作强, 鲁毅, 孙长凯, 林景全. 空气中飞秒激光等离子体荧光辐射光谱研究. 物理学报, 2012, 61(7): 075212. doi: 10.7498/aps.61.075212
    [12] 李盼池, 王海英, 戴庆, 肖红. 量子过程神经网络模型算法及应用. 物理学报, 2012, 61(16): 160303. doi: 10.7498/aps.61.160303
    [13] 蒲昱东, 杨家敏, 靳奉涛, 张璐, 丁永坤. 辐射输运实验中的Al等离子体发射光谱研究. 物理学报, 2011, 60(4): 045210. doi: 10.7498/aps.60.045210
    [14] 于新明, 程书博, 易有根, 张继彦, 蒲昱东, 赵阳, 胡峰, 杨家敏, 郑志坚. Al等离子体类锂伴线的布居机制分析及实验应用. 物理学报, 2011, 60(8): 085201. doi: 10.7498/aps.60.085201
    [15] 李晶, 谢卫平, 黄显宾, 杨礼兵, 蔡红春, 蒲以康. “碰撞-辐射”模型在Z箍缩等离子体K壳层线辐射谱分析中的应用. 物理学报, 2010, 59(11): 7922-7929. doi: 10.7498/aps.59.7922
    [16] 段耀勇, 郭永辉, 邱爱慈, 吴刚. 碰撞辐射稳态等离子体电荷态分布的一种扩展模型. 物理学报, 2010, 59(8): 5588-5595. doi: 10.7498/aps.59.5588
    [17] 徐妙华, 梁天骄, 张 杰. 利用韧致辐射诊断激光等离子体相互作用产生的超热电子. 物理学报, 2006, 55(5): 2357-2363. doi: 10.7498/aps.55.2357
    [18] 张 红, 程新路, 杨向东, 谢方军, 张继彦, 杨国洪. 金等离子体平均离化度随电子温度变化关系的研究. 物理学报, 2003, 52(12): 3098-3101. doi: 10.7498/aps.52.3098
    [19] 马余强, 张玥明, 龚昌德. Hopfield神经网络模型的恢复特性. 物理学报, 1993, 42(8): 1356-1360. doi: 10.7498/aps.42.1356
    [20] 张承福. 等离子体模型碰撞项的比较. 物理学报, 1986, 35(7): 947-952. doi: 10.7498/aps.35.947
计量
  • 文章访问数:  7047
  • PDF下载量:  165
  • 被引次数: 0
出版历程
  • 收稿日期:  2020-12-31
  • 修回日期:  2021-03-14
  • 上网日期:  2021-04-26
  • 刊出日期:  2021-05-05

/

返回文章
返回