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基于机器学习和器件模拟对Cu(In,Ga)Se2电池中Ga含量梯度的优化分析

刘武 朱成皖 李昊天 赵谡玲 乔泊 徐征 宋丹丹

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基于机器学习和器件模拟对Cu(In,Ga)Se2电池中Ga含量梯度的优化分析

刘武, 朱成皖, 李昊天, 赵谡玲, 乔泊, 徐征, 宋丹丹

Optimization of Ga content gradient in Cu(In,Ga)Se2 solar cells through machine learning and device simulation

Liu Wu, Zhu Cheng-Wan, Li Hao-Tian, Zhao Su-Ling, Qiao Bo, Xu Zheng, Song Dan-Dan
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  • Cu(In,Ga)Se2 (CIGS)太阳能电池是一种高效薄膜太阳能电池, Ga含量(Ga/(Ga+In), GGI)梯度调控是在不损失短路电流情况下, 获得高开路电压的一种有效方法. 本文基于对薄膜电池效率极限的对比分析, 首先评估了CIGS电池性能提升的优化空间和策略. 进而, 通过机器学习与电池模拟分析相结合, 研究了不同类别的“V”型GGI梯度对电池性能的影响, 优化了“V”型双梯度的分布, 获得了高于26%的模拟效率, 并探究了其内部载流子作用机理. 本文的研究提供了获得高效率CIGS电池“V”型GGI梯度的优化方案, 为实验优化提供了指导.
    Cu(In,Ga)Se2 (CIGS) solar cell is a kind of highly efficient thin film solar cell, for which Ga ratio (Ga/(Ga+In), GGI) gradient engineering is an efficient approach to achieving high open circuit voltage under no short circuit current loss. In this work, we firstly evaluate the room and the strategies for improving the device performance of the CIGS solar cells based on the comparison among their theoretical efficiency limits. Then we investigate the different schemes of “V” type GGI gradient and their effects on device performance through machine learning and device simulation. Based on these studies, we optimize the scheme of “V” type GGI gradient and obtain a high efficiency of 26% from device simulation. The carrier kinetics for the effect of modifying GGI gradient on device performance are analyzed. This work provides an approach to optimizing the GGI gradient to obtain highly efficient CIGS solar cells, which is referable for experimental optimization.
      通信作者: 宋丹丹, ddsong@bjtu.edu.cn
    • 基金项目: 国家重点研发计划(批准号: 2018YFB1500200)资助的课题
      Corresponding author: Song Dan-Dan, ddsong@bjtu.edu.cn
    • Funds: Project supported by the National Key R&D Program of China (Grant No. 2018YFB1500200).
    [1]

    Nakamura M, Yamaguchi K, Kimoto Y, Yasaki Y, Kato T, Sugimoto H 2019 IEEE J. Photovoltaics 9 1863Google Scholar

    [2]

    Belghachi A, Limam N 2017 Chin. J. Phys. 55 1127Google Scholar

    [3]

    Peace B, Claypoole J, Sun N, Dwyer D, Eisaman M D, Haldar P, Efstathiadis H 2016 J. Alloys Compd. 657 873Google Scholar

    [4]

    Lim D, Kim M Y, Song W 2016 Sci. Adv. Mater. 8 558Google Scholar

    [5]

    Jackson P, Hariskos D, Wuerz R, et al. 2015 Phys. Status Solidi RRL 9 28Google Scholar

    [6]

    Aissani H, Helmaoui A, Moughli H 2017 Int. J. Appl. Eng. Res. 12 227

    [7]

    Saadat M, Moradi M, Zahedifar M 2016 Superlattices. Microstruct. 92 303Google Scholar

    [8]

    Majeed N, Saladina M, Krompiec M, Greedy S, Deibel C, MacKenzie R C I 2020 Adv. Funct. Mater. 30 1907259Google Scholar

    [9]

    Buratti Y, Le Gia Q T, Dick J, Zhu Y, Hameiri Z 2020 NPJ Comput. Mater. 6 1Google Scholar

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    Zhu C W, Liu W, Li Y Y, et al. 2021 Sol. Energy 228 45

    [11]

    Kato T, Wu J L, Hirai Y, Sugimoto H, Bermudez V 2018 IEEE J. Photovoltaics 9 325

    [12]

    Jackson P, Wuerz R, Hariskos D, Lotter E, Witte W, Powalla M 2016 Phys. Status Solidi RRL 10 583Google Scholar

    [13]

    Tai K F, Kamada R, Yagioka T, Kato T, Sugimoto H 2017 Jpn. J. Appl. Phys. 56 08MC03Google Scholar

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    Green M, Dunlop E, Hohl-Ebinger J, Yoshita M, Kopidakis N, Hao X 2021 Prog. Photovoltaics Res. Appl. 29 3Google Scholar

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    Yoshikawa K, Kawasaki H, Yoshida W, Irie T, Konishi K, Nakano K, Uto T, Adachi D, Kanematsu M, Uzu H, Yamamoto K 2017 Nat. Energy 2 1

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    Haase F, Hollemann C, Schäfer S, Merkle A, Rienäcker M, Krügener J, Brendel R, Peibst R 2018 Sol. Energy Mater. Sol. Cells 186 184Google Scholar

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    Yoo J J, Seo G, Chua M R, Park T G, Lu Y, Rotermund F, Kim Y K, Moon C S, Jeon N J, Correa-Baena J P, Bulović V, Shin S S, Bawendi M G, Seo J 2021 Nature 590 587Google Scholar

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    Jeong M, Choi I W, Go E M, Cho Y, Kim M, Lee B, Jeong S, Jo Y, Choi H W, Lee J, Bae J H, Kwak S K, Kim D S, Yang C 2020 Science 369 1615Google Scholar

    [19]

    Green M A, Dunlop E D, Levi D H, Hohl-Ebinger J, Yoshita M, Ho-Baillie A W Y 2019 Prog. Photovoltaics Res. Appl. 27 565Google Scholar

    [20]

    Green M A, Hishikawa Y, Dunlop E D, Levi D H, Hohl-Ebinger J, Yoshita M, Ho-Baillie A W Y 2019 Prog. Photovoltaics Res. Appl. 27 3Google Scholar

    [21]

    Polman A, Knight M, Garnett E C, Ehrler B, Sinke W C 2016 Science 352 aad4424Google Scholar

    [22]

    Nayak P K, Cahen D 2014 Adv. Mater. 26 1622Google Scholar

    [23]

    Jackson P, Hariskos D, Lotter E, Paetel S, Wuerz R, Menner R, Wischmann W, Powalla M 2011 Prog. Photovoltaics Res. Appl. 19 894Google Scholar

    [24]

    Liu Y, Li B, Lin S, Liu W, Adam J, Madsen M, Rubahn H G, Sun Y 2018 J. Phys. Chem. Solids 120 190Google Scholar

    [25]

    Saadat M, Moradi M, Zahedifar M 2016 J. Mater. Sci. -Mater. Electron. 27 1130Google Scholar

    [26]

    Ji S, Hayakawa T, Suyama N, Nakada K, Yamada A 2020 Jpn. J. Appl. Phys. 59 041003Google Scholar

    [27]

    刘芳芳, 孙云, 何青 2014 物理学报 63 047201Google Scholar

    Liu F F, Sun Y, He Q 2014 Acta Phys. Sin. 63 047201Google Scholar

  • 图 1  CIGS吸收层带隙梯度变化

    Fig. 1.  Band gap gradient change of CIGS absorber layer.

    图 2  实验与模拟得到的CIGS太阳能电池J-V特性

    Fig. 2.  J-V characteristic of experimental data and simulation result for the CIGS solar cell.

    图 3  (a) CIGS电池器件结构; (b) CIGS层中GGI梯度示意图

    Fig. 3.  (a) Device structure of the CIGS solar cells; (b) schematic illustration of GGI gradient in CIGS.

    图 4  RF算法预测的电池效率随GGIF/GGIM/GGIB变化的曲线

    Fig. 4.  Changes of predicted efficiency by RF algorithm with GGIF/GGIM/GGIB.

    图 5  RF算法预测的不同GGI值情况下的器件效率

    Fig. 5.  Device efficiency predicted by RF algorithm under different GGI values.

    图 6  RF算法预测的不同GGI梯度的器件性能. 其中, 0代表实验效率为23.35%的电池所采用的GGI梯度, 模型1—模型6为预测效率最高的6组器件的GGI梯度

    Fig. 6.  Predicted device performance of the CIGS solar cells based on different GGI gradients by RF algorithm. 0 represents for the GGI gradient used in the device with 23.35% experimental efficiency, while 1–6 are the GGI gradients of the devices with the highest predicted efficiency.

    图 7  器件模拟采用的14种“V”型双梯度模型 (a) GGIF = 0.4; (b) GGIF = 0.5; (c) GGIF = 0.6

    Fig. 7.  14 types of “V”-shaped double GGI gradient schemes used in the device simulation: (a) GGIF = 0.4; (b) GGIF = 0.5; (c) GGIF = 0.6.

    图 8  器件模拟得到的不同GGI梯度模型的器件性能

    Fig. 8.  Device performance of the CIGS solar cells based on different GGI gradient schemes obtained by device simulation.

    图 9  器件模拟得到的CIGS电池不同位置载流子复合速率 (a) 前表面; (b) 电池内部; (c) 后表面. R为复合速率, 单位cm–3·s–1

    Fig. 9.  Carrier recombination rate at different positions of CIGS solar cells obtained by device simulation: (a) Front surface; (b) interior; (c) rear surface. Here, R is the recombination rate, which unit is cm–3·s–1.

    图 10  器件模拟得到的CIGS薄膜背电场强度

    Fig. 10.  Back electric field intensity of CIGS film obtained by device simulation.

    表 1  CIGS, c-Si, 钙钛矿三种电池的光伏特性参数、S-Q极限值及差值分析. Eg, PCE/VOC/JSC/FF, JSQ, WOC分别代表材料的带隙、电池的转换效率/开路电压/短路电流密度/填充因子、SQ极限计算的短路电流密度、VOC损失

    Table 1.  Photovoltaic parameters, S-Q limit value and difference analysis of CIGS, c-Si, and perovskite solar cells. Here, Eg, PCE/VOC/JSC/FF, JSQ, WOC respectively represent the band gap of the absorption material, the photoelectric conversion efficiency/open circuit voltage/short-circuit current density/fill factor of the solar cells, the short-circuit current density calculated by the SQ limit, and the VOC loss.

    Cell typeEth/eVPCE/%VOC/mVJSC/(mA·cm–2)FF/%JSQ/(mA·cm–2)WOC/mVJSC/JSQ/%机构参考文献
    CIGS1.08823.35734.039.5880.444.0354.089.95SF[1]
    CIGS1.13022.92746.038.5079.743.0384.089.53SF[11]
    CIGS1.14322.60741.037.8080.642.3402.089.36ZSW[12]
    CIGS1.11022.30722.039.4078.243.7388.090.16SF[13]
    c-Si1.12026.70738.042.6584.943.3382.098.50Kaneka[14]
    c-Si1.12026.60740.342.5084.743.3379.798.15Kaneka[14]
    c-Si1.12026.30744.042.3083.843.3376.097.69Kaneka[15]
    c-Si1.12026.10726.642.6284.343.3393.498.43ISFH[16]
    钙钛矿1.55025.201180.525.1484.827.1369.592.77KRICT[17]
    钙钛矿1.48024.641181.426.1879.629.5298.683.53UNIST[18]
    钙钛矿1.57424.201194.824.1684.026.2379.292.21KRICT[19]
    钙钛矿1.53623.701169.725.4079.827.6366.392.03ISCAS[20]
    下载: 导出CSV

    表 2  器件模拟中使用的参数设置

    Table 2.  Parameter settings used in device simulation.

    B:ZnOZn(Mg, O)Zn(O, S, OH)xCIGS
    Thickness/nm5005081000
    Permittivity/1910913.6
    Eg/eV3.33.63.61.06—1.66
    Affinity/eV4.44.24.23.89—4.49
    Nc/(1018 cm–3)2.22.22.22.2
    Nv/(1019 cm–3)1.81.81.81.8
    µn/(cm2·V–1·s–1)100100100100
    µp/(cm2·V–1·s–1)25252525
    Nd/(1017 cm–3)101010
    Na/(1016 cm–3)0002
    下载: 导出CSV

    表 3  不同位置GGI与光伏参数之间的相关性

    Table 3.  Correlation between GGI value at different location and photovoltaic parameters.

    GGIFGGIMGGIB
    VOC0.380.350.34
    JSC–0.26–0.41–0.12
    下载: 导出CSV

    表 4  wxAMPS器件模拟所用的GGI梯度模型

    Table 4.  Schemes of GGI gradient used for wxAMPS device simulation.

    模型1234567
    GGIF0.400.400.400.400.400.400.50
    GGIM0.100.150.200.250.300.350.10
    GGIB0.840.770.710.650.580.520.80
    模型891011121314
    GGIF0.500.500.500.600.600.600.60
    GGIM0.200.300.400.100.200.300.40
    GGIB0.670.540.420.760.630.500.44
    下载: 导出CSV
  • [1]

    Nakamura M, Yamaguchi K, Kimoto Y, Yasaki Y, Kato T, Sugimoto H 2019 IEEE J. Photovoltaics 9 1863Google Scholar

    [2]

    Belghachi A, Limam N 2017 Chin. J. Phys. 55 1127Google Scholar

    [3]

    Peace B, Claypoole J, Sun N, Dwyer D, Eisaman M D, Haldar P, Efstathiadis H 2016 J. Alloys Compd. 657 873Google Scholar

    [4]

    Lim D, Kim M Y, Song W 2016 Sci. Adv. Mater. 8 558Google Scholar

    [5]

    Jackson P, Hariskos D, Wuerz R, et al. 2015 Phys. Status Solidi RRL 9 28Google Scholar

    [6]

    Aissani H, Helmaoui A, Moughli H 2017 Int. J. Appl. Eng. Res. 12 227

    [7]

    Saadat M, Moradi M, Zahedifar M 2016 Superlattices. Microstruct. 92 303Google Scholar

    [8]

    Majeed N, Saladina M, Krompiec M, Greedy S, Deibel C, MacKenzie R C I 2020 Adv. Funct. Mater. 30 1907259Google Scholar

    [9]

    Buratti Y, Le Gia Q T, Dick J, Zhu Y, Hameiri Z 2020 NPJ Comput. Mater. 6 1Google Scholar

    [10]

    Zhu C W, Liu W, Li Y Y, et al. 2021 Sol. Energy 228 45

    [11]

    Kato T, Wu J L, Hirai Y, Sugimoto H, Bermudez V 2018 IEEE J. Photovoltaics 9 325

    [12]

    Jackson P, Wuerz R, Hariskos D, Lotter E, Witte W, Powalla M 2016 Phys. Status Solidi RRL 10 583Google Scholar

    [13]

    Tai K F, Kamada R, Yagioka T, Kato T, Sugimoto H 2017 Jpn. J. Appl. Phys. 56 08MC03Google Scholar

    [14]

    Green M, Dunlop E, Hohl-Ebinger J, Yoshita M, Kopidakis N, Hao X 2021 Prog. Photovoltaics Res. Appl. 29 3Google Scholar

    [15]

    Yoshikawa K, Kawasaki H, Yoshida W, Irie T, Konishi K, Nakano K, Uto T, Adachi D, Kanematsu M, Uzu H, Yamamoto K 2017 Nat. Energy 2 1

    [16]

    Haase F, Hollemann C, Schäfer S, Merkle A, Rienäcker M, Krügener J, Brendel R, Peibst R 2018 Sol. Energy Mater. Sol. Cells 186 184Google Scholar

    [17]

    Yoo J J, Seo G, Chua M R, Park T G, Lu Y, Rotermund F, Kim Y K, Moon C S, Jeon N J, Correa-Baena J P, Bulović V, Shin S S, Bawendi M G, Seo J 2021 Nature 590 587Google Scholar

    [18]

    Jeong M, Choi I W, Go E M, Cho Y, Kim M, Lee B, Jeong S, Jo Y, Choi H W, Lee J, Bae J H, Kwak S K, Kim D S, Yang C 2020 Science 369 1615Google Scholar

    [19]

    Green M A, Dunlop E D, Levi D H, Hohl-Ebinger J, Yoshita M, Ho-Baillie A W Y 2019 Prog. Photovoltaics Res. Appl. 27 565Google Scholar

    [20]

    Green M A, Hishikawa Y, Dunlop E D, Levi D H, Hohl-Ebinger J, Yoshita M, Ho-Baillie A W Y 2019 Prog. Photovoltaics Res. Appl. 27 3Google Scholar

    [21]

    Polman A, Knight M, Garnett E C, Ehrler B, Sinke W C 2016 Science 352 aad4424Google Scholar

    [22]

    Nayak P K, Cahen D 2014 Adv. Mater. 26 1622Google Scholar

    [23]

    Jackson P, Hariskos D, Lotter E, Paetel S, Wuerz R, Menner R, Wischmann W, Powalla M 2011 Prog. Photovoltaics Res. Appl. 19 894Google Scholar

    [24]

    Liu Y, Li B, Lin S, Liu W, Adam J, Madsen M, Rubahn H G, Sun Y 2018 J. Phys. Chem. Solids 120 190Google Scholar

    [25]

    Saadat M, Moradi M, Zahedifar M 2016 J. Mater. Sci. -Mater. Electron. 27 1130Google Scholar

    [26]

    Ji S, Hayakawa T, Suyama N, Nakada K, Yamada A 2020 Jpn. J. Appl. Phys. 59 041003Google Scholar

    [27]

    刘芳芳, 孙云, 何青 2014 物理学报 63 047201Google Scholar

    Liu F F, Sun Y, He Q 2014 Acta Phys. Sin. 63 047201Google Scholar

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出版历程
  • 收稿日期:  2021-07-01
  • 修回日期:  2021-07-31
  • 上网日期:  2021-08-20
  • 刊出日期:  2021-12-05

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