Development of Highly Efficient Tandem White OLEDs
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摘要: 叠层有机发光二极管（Organic Light-Emitting Diode，OLED）白光器件具备低功耗、高亮度、高色域等性能优势。然而，由于效率、寿命及驱动电压等性能仍有较大改进空间，叠层结构的材料及电学结构仍需进一步优化。本文重点介绍叠层OLED白光器件的最新研究进展，总结了三类电荷产生层（Charge Generation Layer，CGL）在工程化应用中存在的问题以及其非破坏性检测方法；综述高效叠层OLED白光器件的“全磷光体系”、“并行通道激子收集”及“混合磷光热活性型延迟荧光（Thermally Activated Delayed Fluorescence，TADF）体系”最新研究成果，对器件寿命问题进行总结，探讨分析“分级掺杂”、“四色混合TADF体系”等从结构方面提出优化方案，并针对不同发光材料体系中的CGL材料及结构综述叠层OLED白光器件实现较低工作电压的技术方法，最后对叠层OLED白光器件的材料和结构提出改进建议。
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关键词:
- 叠层白光有机发光二极管 /
- 电荷产生层 /
- 有机发光单元 /
- 功能层结构 /
- 有机发光材料
Abstract: Tandem white OLEDs offer low power consumption, high brightness, and a high color gamut. However, the material and electrical structures of tandem white OLEDs still need to be optimized owing to the outstanding challenges in efficiency, lifetime, and driving voltage. In this study, we focused on the latest research on tandem white OLEDs and summarized the problems in engineering preparation and non-destructive detection method of 3 types of CGLs for high-efficiency tandem white OLEDs. We focused on the latest research on the "all-phosphorescent system, " "harvesting excitons via two parallel channels, " and the "mixed-phosphorescent-TADF system" simultaneously. We summarized the device lifetime problems and discussed structural solutions such as "graded doping" and "four-color mixed-phosphorescent-TADF system." From the aspect of CGL materials and structures in different systems, we reviewed the scheme of lower driving voltage for tandem white OLEDs. Finally, we provided suggestions for improving the materials and structures of tandem white OLEDs. -
0. 引言
红外热成像系统工作在较为严酷的使用环境中时，受安装力、高低温和冲击振动等因素影响，内部的机械支撑结构产生微小的变形，使光学元件出现空间状态的偏移（包含偏心或倾斜），导致系统的光轴发生漂移，影响系统对目标的指示精度[1-6]。红外折转光学系统具有体积小、布局紧凑的优势，但在工程实践中发现，相较于直线形光学系统，光轴在环境影响下的漂移量相对更大。为了满足使用要求，光轴的最大漂移角度即光轴稳定性必须限定在允许范围之内。因此，在系统设计阶段，需严格分析各光学元件的偏移误差对系统光轴带来的影响，识别出光学系统在静态下的敏感点，同时基于光轴稳定性指标分析出各个镜片偏心和倾斜的合理公差限，从而能够得到光学系统所容许各支撑结构件最大的形变范围，以作为结构优化的约束条件[7-14]，为光轴稳定性的设计提供指导。
对于折转光学系统，由于其非旋转对称性，光学元件的偏移状态需要考虑偏移量和偏移方向两方面信息。也就是说，在分析某个元件允许的最大偏心量或倾斜量时，需要对所有可能的偏心方向或倾斜方向进行采样，选取最严苛的情况作为结构优化的约束。国内外有关光学系统公差灵敏度的研究，在考虑元件的偏心量或倾斜量时大多仅针对X-轴方向或Y-轴方向进行采样，而极少考虑其他倾斜方向，对折转光学系统的适用性较低[15-17]。
目前主流的光学软件具有完备分析公差的流程，但其对光学元件倾斜状态的建模是先后绕X、Y、Z各个坐标轴的旋转形成，其旋转值为过程量，不能代表旋转后形成的空间状态量[18-19]。利用现有软件进行公差分析时，只能完全按照建模的输入方式，对光学元件旋转过程量采样，无法对其倾斜的空间状态量采样；而对结构优化来说，所需的约束条件为状态量，这就导致采样结果与约束条件不能对应，对结构设计的指导存在局限性。
本文基于公差分析的思想，立足于红外折转光学系统元件的偏心、倾斜静态公差，建立光学元件旋转过程量与旋转形成的空间状态量（即倾斜量和倾斜方向）之间的转换关系，实现对光学元件在任意方向倾斜状态的准确采样；在此基础上，建立光学元件偏移对光轴稳定性的影响的灵敏度分析、反灵敏度分析及蒙特卡罗分析整个公差计算流程，以获得折转光学系统的静态敏感点、以及结构优化设计所需的约束条件。
1. 光学元件旋转过程量和倾斜状态量的转换关系建立
蒙特卡罗公差分析是光轴静态敏感度分析中最为重要的一步，是最终获得光学系统中各镜片偏心和倾斜合理公差限的必要过程。在红外折转光学系统光轴敏感度公差的蒙特卡罗分析中，为了和结构优化设计能够对应，需在每个元件垂直于光轴的参考平面的360°所有方向上，对倾斜量进行蒙特卡罗采样，然后根据采样的状态构建出对应的倾斜模型，从而能够对该模型进行非顺序光线追迹，计算出光轴的最终漂移情况。
对倾斜模型的构建，需要通过旋转元件来完成。在几何空间中，每个元件自带一个右手局部坐标系O-XYZ，其中Z-轴沿光轴方向。元件的旋转运动姿态采用欧拉角来表述，旋转后的最终姿态除了与角度相关，还与旋转顺序相关[20]。在对元件的倾斜进行建模时，假设旋转按图 1所规定的顺序，即元件先绕自身坐标轴的X-轴左旋α角度，再绕旋转后形成的坐标轴的Y-轴左旋β角度，再绕旋转后形成的坐标轴的Z-轴右旋γ角度，得到元件的新坐标系O-X1Y1Z1，则空间中某点W在原坐标系O-XYZ和新坐标系O-X1Y1Z1中的坐标描述对应关系为：
$$ \begin{array}{l} \left[ {\begin{array}{*{20}{c}} X \\ Y \\ Z \end{array}} \right] = \left[ {\begin{array}{*{20}{c}} {\text{1}}&{\text{0}}&{\text{0}} \\ {\text{0}}&{\cos \alpha }&{\sin \alpha } \\ {\text{0}}&{ - \sin \alpha }&{\cos \alpha } \end{array}} \right]\left[ {\begin{array}{*{20}{c}} {\cos \beta }&0&{ - \sin \beta } \\ 0&1&0 \\ {\sin \beta }&0&{\cos \beta } \end{array}} \right] \cdot \hfill \\ \quad \quad \quad \;\;\, \left[ {\begin{array}{*{20}{c}} {\cos \gamma }&{ - {\text{s}}in\gamma }&{\text{0}} \\ {\sin \gamma }&{\cos \gamma }&0 \\ {\text{0}}&0&{\text{1}} \end{array}} \right]\left[ {\begin{array}{*{20}{c}} {{X_1}} \\ {{Y_1}} \\ {{Z_1}} \end{array}} \right] \hfill \\ \end{array} $$ (1) 式中：[X, Y, Z]是点W在原坐标系O-XYZ中的坐标，[X1, Y1, Z1]是点W在旋转后坐标系O-X1Y1Z1中的坐标。
旋转后，新Z-轴的方向向量为$ {\vec n_1} $，将O-X1Y1Z1中的Z-轴的单位向量[0, 0, 1]代入上式中[X1, Y1, Z1]，可求出经过旋转后，$ {\vec n_1} $在原始坐标系O-XYZ下的向量坐标[X, Y, Z]：
$$ \left[ {\begin{array}{*{20}{c}} X \\ Y \\ Z \end{array}} \right] = \left[ {\begin{array}{*{20}{c}} { - \sin \beta } \\ {\cos \beta \cdot \sin \alpha } \\ {\cos \alpha \cdot \cos \beta } \end{array}} \right] $$ (2) 由于Z-轴为元件的光轴方向，因此绕Z-轴的旋转并不影响方向向量最后的朝向，所以由式(2)可以看到，方程组是一个与γ无关的等式。
此时，若要描述元件旋转后的倾斜姿态，可使用空间状态量，即描述倾斜方向的u角度，以及描述倾斜量值的v角度，如图 2所示，其中，倾斜角v代表光学元件倾斜前后的夹角，方向角u代表元件倾斜的参考轴线与原坐标系Y-轴的夹角。经过旋转后的$ {\vec n_1} $在原始坐标系O-XYZ下的向量坐标[X, Y, Z]也可以由状态量u、v表示：
$$ \left[ {\begin{array}{*{20}{c}} X \\ Y \\ Z \end{array}} \right] = \left[ {\begin{array}{*{20}{c}} { - \cos u \cdot \sin v} \\ {\sin u \cdot \sin v} \\ {\cos v} \end{array}} \right] $$ (3) 如果要实现任意方向倾斜采样的蒙特卡罗分析，需要建立光学元件旋转过程α，β和空间状态量的u，v之间的转换关系。因此联立式(2)与式(3)，得到了u，v与α，β之间的对应关系：
$$ \left\{ \begin{gathered} \beta = \arcsin (\cos u \cdot \sin v) \hfill \\ \alpha = \arcsin (\frac{{\sin u \cdot \sin v}}{{\cos b}}) \hfill \\ \end{gathered} \right. $$ (4) 这样，通过旋转矩阵的推导，解出了光学元件倾斜状态量（方向角u、倾斜角v）到倾斜建模过程量（旋转角α，β）的转换对应公式。在任意方向蒙特卡罗分析中，只需对方向角u和倾斜角v进行采样，就可利用上述公式将采样数据转换建模需要的旋转角α，β，从而构建出元件倾斜的模型，进行非顺序光线追迹。
2. 光轴静态敏感度分析流程构建
2.1 基本思路
对红外折转光学系统光轴静态敏感度分析，本质上是公差分析，需将光学元件的偏心、倾斜公差引入光学系统中，分析由元件偏移引起的光轴漂移与光轴稳定性指标的匹配情况。构建红外折转光学系统的光轴敏感度公差分析流程的基本思路如图 3所示，输入为光学系统的仿真模型以及系统的光轴稳定性指标，经过光轴敏感度分析以后，最终输出的是光学系统中各个元件合理的公差限数据。该思路对光轴敏感度分析分解为三步：灵敏度分析-反灵敏度分析-蒙特卡罗分析[21]，每一步的分析需要基于上一步分析的结果数据作为输入，而输出的数据将作为下一步分析的输入。
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图 3 构建红外光学系统光轴敏感度公差分析流程的基本思路Figure 3. Basic idea of constructing tolerance analysis flow of optical axis sensitivity for infrared optical system流程中首先开展灵敏度公差分析，根据光轴稳定性指标进行初步分解，将光学系统的公差大致设定在某个范围，用非顺序建模实现对光学元件空间姿态的精确模拟，并通过非顺序光线追迹，仿真计算这些公差所造成的光轴漂移量数据（如图 4所示），此数据即为各元件偏心和倾斜的光轴灵敏度数据，以此识别敏感元件。
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图 4 透镜倾斜(a)和透镜偏心(b)引起的光轴漂移Figure 4. Optical axis shift caused by tilted lens (a) and decentered lens (b)反灵敏度分析则假设光学系统的评价标准（即光轴漂移量）变化特定的数值，评估造成相应变化的各项公差范围，称之为光轴反灵敏度数据，可根据上一步光轴灵敏度数据的多次计算迭代求得。
光轴的反灵敏度数据，可作为蒙特卡罗公差分析中的输入，即各光学件偏心和倾斜的光差限初值，以公差限初值作为反射镜各运动分量的蒙特卡洛采样区间，在区间内进行各元件偏心和倾斜随机取值并叠加成组合误差，进行了蒙特卡洛分析，得出光轴漂移量的初步统计评估结果，与系统的光轴稳定性的指标要求对比后，放宽或收紧采样区间，进行多轮分析，最终得到合理的公差限。
2.2 程序设计
根据上述思路，对光学系统进行光轴敏感度分析的程序流程如图 5所示，基于Matlab编制计算程序，通过调用LightTools软件实现光学元件偏移及非顺序光线追迹，从而完成光轴漂移仿真数据计算。该流程以光学系统的光轴稳定性指标Iaxis作为流程输入，以光学系统中各镜片的偏心和倾斜合理公差限为输出。
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图 5 光轴稳定性公差分析流程Figure 5. Flow diagram for tolerance analysis of stability of optical axis程序中，调取各透镜的原始偏心值D0和原始倾斜值T0，分别叠加标准偏心值Dstan和标准倾斜值Tstan，通过对轴上主光线追迹计算光轴漂移，并进行公差分析，得到光学元件的灵敏度数据。基于光学元件的灵敏度数据和系统的光轴稳定性指标Iaxis，计算出光学元件的反灵敏度数据。基于光学元件的光轴反灵敏度数据，得到公差限初值ϕ0，即光学元件各运动分量的蒙特卡洛采样区间。给定循环次数Kloop，在公差限初值范围内对每一光学件的偏心方向u，偏心量v，旋转方向m，旋转量n四个值分别产生Kloop组随机数。偏心方向u和旋转方向v在0~360°的范围内采样，偏心量v和旋转量n则在公差限初值的范围内采样。由偏心方向u，偏心量v，旋转方向m，旋转量n四个值，通过上节中的数据变换，计算得出光学元件建模所需的过程量输入，即沿X-轴方向的平移量x，沿Y方向的平移量y，绕X-轴的旋转量α，绕X-轴的旋转量β。
m，n与x，y的对应关系可由简单的三角函数求得：
$$ \left\{ \begin{gathered} x = n \cdot \cos m \hfill \\ y = n \cdot \sin m \hfill \\ \end{gathered} \right. $$ (5) 式中：u，v与α，β的对应关系由上节中推导得出的光学元件旋转过程量和倾斜状态量的转换关系式(4)求得。
基于计算得出的位置与旋转角度输入，进行光学元件偏移状态的建模和光轴漂移的仿真分析，得到各光学元件偏心与倾斜随机采样数据搭配后的光线输出数据，对输出数据进行统计分析，得出光轴漂移量的蒙特卡洛分析结果。
将输出的蒙特卡罗分析结果与光轴漂移量的指标要求进行对比，得出新一轮蒙特卡罗分析的公差限调整系数Pn+1，如下式计算：
$$ P_{n+1}＝I_{{\rm{axis}}}/I_{n}$$ (6) 式中：In为本轮光轴漂移量蒙特卡洛分析结果。若Pn+1已接近1，则说明输入的公差限已经合理，无需再调整。否则，则需按照下式放宽或收紧公差限：
$$ ϕ_{n+1}＝P_{n+1}⋅ϕ_{n}$$ (7) 式中：ϕn为本轮的公差限；ϕn+1为新一轮公差限。获取新一轮公差限后，再次展开蒙特卡罗分析，重复迭代此过程，直至公差限调整系数P接近1，即光轴漂移量的蒙特卡罗统计结果与系统光轴稳定性指标要求接近，则可将该轮的公差限作为光学元件偏移的约束值。
3. 光轴敏感度分析实例
根据上述搭建的光轴敏感度分析流程和计算程序，选取了光轴较为敏感的某型红外热成像光学系统作为分析对象，光学系统的仿真模型如图 6所示。该系统为制冷型中波热像仪，为减少体积，采用了两个折转反射镜形成U型折转光路。本例以系统的光轴稳定性指标Iaxis≤0.25 mrad为例，对系统中8个光学透镜和2个折转反射镜的倾斜和偏心的公差限进行分析计算。
在倾斜和偏心的灵敏度分析中，本例的标准偏心值Dstan设置为0.01 mm，标准倾斜值Tstan设置为0.01°。在倾斜和偏心的反灵敏度分析中，由于系统的光轴稳定性指标Iaxis≤0.25 mrad，而光学系统中包括反射镜在内，共有10个光学件，且每个光学件包含两个公差（偏心和倾斜），这样一来，共由20个公差对光轴漂移产生贡献，因此，将0.25 mrad的指标均分成20份，以光轴漂移量为0.0125 mrad作为反灵敏度分析的标准。
图 7(a)为倾斜灵敏度分析结果，横坐标是光学元件的序号，纵坐标是光轴漂移量；图 7(b)为倾斜反灵敏度分析结果，横坐标是光学元件的序号，纵坐标是光学件的倾斜量。从倾斜灵敏度折线图中可以较为直观地看出，当每个透镜同时倾斜0.01°，对光轴漂移量产生的贡献存在很大差异。对比倾斜灵敏度折线图与倾斜反灵敏度折线图中可以发现，倾斜反灵敏度的数据与倾斜灵敏度数据正好相反，第2反射镜的倾斜灵敏度最高，倾斜反灵敏度最低，第5透镜的倾斜灵敏度最低，倾斜反灵敏度最高。从图中能够识别出，第1反射镜和第2反射镜是对倾斜最敏感的关键元件。
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图 7 倾斜灵敏度(a)和倾斜反灵敏度(b)折线图Figure 7. Line chart of tilt sensitivity(a) and tilt anti-sensitivity(b)图 8(a)为偏心灵敏度分析结果，横坐标是光学件的序号，纵坐标是光轴漂移量；图 8(b)为偏心反灵敏度分析结果，横坐标是光学件的序号，纵坐标是光学件的偏心量。对比偏心灵敏度折线图与偏心反灵敏度折线图中可以发现，偏心反灵敏度的数据与偏心灵敏度数据同样正好相反，第2透镜偏心灵敏度最高，偏心反灵敏度最低，第5透镜的偏心灵敏度最低，偏心反灵敏度最高。从图中能够识别出，第2透镜和第3透镜是对偏心最敏感的关键元件。
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图 8 偏心灵敏度(a)和偏心反灵敏度(b)折线图Figure 8. Line chart of decenter sensitivity(a) and decenter anti-sensitivity(b)将光轴反灵敏度分析数据设置为各光学元件偏心和倾斜的初始公差限，在8个透镜与2个反射镜的偏心与倾斜初始公差限范围内进行蒙特卡罗分析，本例中循环次数Kloop设置为10000次。图 9(a)为蒙特卡罗分析后输出的10000组光学系统光轴漂移量数据的分布区间直方图，横坐标为光轴漂移量，纵坐标为分布在相应光轴漂移量区间的蒙特卡罗实验次数，由图可知，分布在光轴漂移量为0.021 mrad附近区间的实验次数最多。图 9(b)为数据的累积分布函数图，横坐标为光轴漂移量，纵坐标为累计分布概率，可以看到，50%的概率光轴移动量在0.025 mrad范围内，70%的概率光轴移动量在0.033 mrad范围内，90%的概率光轴移动量在0.0456 mrad范围内。
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图 9 光轴漂移量分布直方图(a)和累积分布图(b)Figure 9. Distribution histogram (a) and cumulative distribution (b) of optical axis shift本文中以90%的概率为例，因此基于初始公差限，光学系统的光轴移动量评估结果大概率分布在0.0456 mrad范围内，而对比本例设置的指标，系统光轴稳定性Iaxis≤0.25 mrad。因此基于蒙特卡罗分析评估的光轴漂移量结果还远低于光轴一致性指标，说明设置的初始公差限过于保守，因此按照式(6)计算，公差调整系数等于5.48，并按照式(7)放宽各光学件的偏心与倾斜公差限。
根据调整后的公差限，进行蒙特卡罗随机采样。新一轮蒙特卡罗分析的输出数据分布区间直方图及累积分布函数图如图 10(a)和图 10(b)所示。
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图 10 调整公差限后的光轴漂移量分布直方图(a)和光轴漂移量累积分布图(b)Figure 10. Distribution histogram (a) and cumulative distribution (b) of optical axis shift after adjusting tolerance本轮输出的光轴漂移量数据以90%的概率分布在0~0.256 mrad范围内，基本与光学系统的光轴一致性指标相匹配。因此将本轮输入的公差限作为最终各光学件的倾斜和偏心合理公差。
表 1为采用本文程序对目标光学系统依次进行了灵敏度分析、反灵敏度分析、蒙特卡罗分析之后，得出的各个光学元件的合理公差限。此公差限可作为结构优化设计的约束条件，只要由结构件引起的光学元件偏心和倾斜不超过表 1数据的范围，即可保证光学系统的光轴漂移量不超出系统光轴稳定性0.25 mrad的指标要求。从表 1数据和前面灵敏度、反灵敏度分析结果可以看出，结构优化设计需要着重考虑高低温或外力的作用下，第1、2反射镜的倾斜、以及第2、3透镜的偏心。因此，该分析识别出了光学系统的敏感点，同时能够指导结构优化设计，以满足光轴稳定性指标。
表 1 各光学件倾斜与偏心最终公差限Table 1. Final tolerances of tilt and decenter of each optical componentLENS Tilt tolerance/° Decenter tolerance/mm Lens 1 0.17811389 0.01035527 Lens 2 0.19549745 0.00539823 Lens 3 0.15172749 0.00718520 Lens 4 1.50604475 0.01545493 Lens 5 1.55067910 0.02021238 Mirror 1 0.02377837 - Mirror 2 0.01895890 - Lens 6 0.23078320 0.00937822 Lens 7 0.11583835 0.00953447 Lens 8 1.05775137 0.01556741 4. 数据准确度验证
本文为实现任意角度倾斜的蒙特卡罗分析，采用数据转换的思路，利用坐标变换推导出了方向角和倾斜角到旋转建模角度的转换关系式。为了对文中角度转换公式的准确性进行验证，利用上一章实例中输出的公差限，并通过上文中提到的通过手动建立多重临时坐标系的替代方法，对实例中光学系统再次进行蒙特卡洛分析，观察是否能得出一致的光轴移动量分布数据。
在仿真软件中，如图 11所示，根据每一个光学元件的原始坐标系分别建立一个相同的临时坐标系，并使临时坐标系绕Y′-轴左旋90°旋转，使临时坐标系的X′-轴朝向原内层坐标系的Z-轴，临时坐标系的Y′-轴朝向原内层坐标系的Y-轴，临时坐标系的Z′-轴朝向原内层坐标系的X-轴负方向，将原始坐标系嵌套在临时坐标系的内层并绑定，让外层坐标系带动光学元件一同旋转，实现让光学件先绕光轴旋转，在绕与光轴垂直方向的坐标轴旋转。
以实例中基于最后一轮公差限产生随机采样数据，输入到临时坐标系中，进行蒙特卡洛分析。得到的数据分布区间直方图如图 12(a)，与上一章中得出的数据分布区间直方图如图 12(b)进行对比，可以看出，两种方法得出的结果分布完全一致，证明了数据转换方法的准确性。
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图 12 数据转换方法得出(a)和临时坐标系方法得出(b)的分布直方图Figure 12. Histogram of distribution obtained by data conversion method(a) and temporary coordinate system method (b)此外，相较于手动建立多重临时坐标系的方法，本文建立的方法适用性更强、效率更高，省去了很多繁琐过程，不需要为每一个新的光学系统的所有光学元件手动建立与其坐标相对应的两重临时坐标系，避免了数据输入或匹配错误。
5. 结论
在对红外热成像折转光学系统进行光轴稳定性的结构优化过程中，非常有必要针对光学系统开展光轴的静态敏感度分析，基于系统的光轴稳定性指标分析出各个光学元件偏心和倾斜的合理公差限，从而指导结构优化。为解决蒙特卡罗分析中对折转光学系统元件的任意方向倾斜采样的难点，通过基于旋转矩阵的坐标变换，建立了光学元件旋转过程量α，β和空间状态量的u，v相互转换的方法，使蒙特卡罗分析能够模拟真实情况下的光学元件运动，计算效率高且具有通用性。在建立的数据转换方法的基础上，搭建了对红外光学系统光轴静态敏感度分析的流程，并运用Matlab对LightTools进行二次开发，实现了程序化。与此同时，以一个光轴较为敏感的实际光学系统为例，运用建立的方法对其开展了光轴静态敏感度分析，得出了光学系统的敏感点和各光学元件的合理公差限，并结合建立临时外重坐标系的方法，对数据准确性进行了验证，证实了公式推导以及程序的可靠性和准确性。
文中所建立的光轴静态敏感度分析流程为实现完整的光机热优化设计奠定了基础，它虽然是针对光学系统的光轴分析所搭建的，但与此同时，文中提出的任意方向倾斜的蒙特卡罗采样方法也为折转光学系统像质的公差分析提供了解决思路。后续将对如何优化文中方法分析出的敏感元件开展研究，以期进一步提高光学系统的光轴稳定性。
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图 3 OLED电致发光结构示意图 (a)单层白光OLED器件，(b)多层白光OLED器件，(c)叠层白光OLED器件
Figure 3. OLED electroluminescence structure diagram (a) Single EML white OLEDs, (b) Multiple EMLs white OLEDs, (c) Tandem white OLEDs
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图 4 CGL能级性质及CGL性能检测方法 (a)分别为Alq3/Mg: Alq3、Alq3/Yb: Alq3和Alq3-Ca: Alq3界面的能级图[75]，(b) CGL器件示意图，(c) CGL器件的C-V分析(@100Hz)[79]
Figure 4. CGL energy level properties and CGL performance testing methods (a) Energy level diagrams for Alq3/Mg: Alq3, Alq3/Yb: Alq3 and Alq3/Ca: Alq3 interfaces, respectively[75], (b) Schematic diagram of a CGL device, (c) C-V analysis of the CGL device (@100 Hz)[79]
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图 5 磷光叠层OLED白光器件的光电性能 (a)首个磷光叠层OLED白光器件的发光光谱随电压变化的趋势[61]，(b) 器件1和器件2的电流效率/外量子效率与电流密度之间的关系[92]
Figure 5. Electroluminescent properties of phosphorescent tandem OLEDs (a) Trending of luminescence spectrum with voltage in the first phosphorescent tandem OLEDs[61], (b) Relationship between current efficiency/external quantum efficiency and current density for device 1 and device 2[92]
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图 6 磷光叠层OLED白光器件的能级、结构及光电性能 (a) 采用Ir(ppy)3及PQIr共掺杂CBP主体材料红绿光以及FlzIr掺杂mCP主体材料蓝绿光的器件材料及能级结构示意图(插图：FlzIr结构式)[64]；(b) 采用蓝色磷光掺杂材料FIrpic以及橙色磷光掺杂材料(fbi)2Ir(acac)共掺杂mCP主体材料中的高效叠层有机磷光发光白光器件的功率效率、外量子效率(插图：电压-亮度及电压-电流密度图)[97]以及结构示意图[98]
Figure 6. Energy level, functional layer structure and electroluminescent properties of phosphorescent tandem OLEDs (a) The device material and energy level structure of Ir(ppy)3 and PQIr co-doped CBP main material red-green light and FlzIr doped mCP main material blue-green light are shown (Inset: FlzIr structure formula)[64]; (b) Power efficiency, external quantum efficiency (Inset: voltage-brightness and voltage-current density plots)[97] and structural schematic[98] of highly efficient tandem white OLEDs using FIrpic and (fbi)2Ir(acac) co-doped mCP bodies
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图 7 采用TCTA: 三[4-咔唑-9-基苯基]胺（TCTA）: 4, 6-双(3, 5-二(3-吡啶)基苯基)-2-甲基嘧啶(B3PYMPM: Ir(ppy)2(tmd)): 二[2-苯喹啉]四甲基庚二酸铱(Ir(mphmq)2(tmd))的橙色磷光EML以及mCP: B3PYMPM: FIrpic的蓝色磷光EML的叠层磷光OLED白光器件结构及优化 (a)功能层结构；(b) 基于经典偶极子模型对橙色磷光OLED器件以及叠层磷光OLED白光器件进行模拟优化[99]
Figure 7. Structure and optimization of tandem white OLEDs' structure with TCTA: B3PYMPM: Ir(ppy)2(tmd): Ir(mphmq)2(tmd) for orange phosphorescent EML and mCP: B3PYMPM: FIrpic for blue phosphorescent EML (a) Functional layer structure; (b) Optimization of devices based on the classical dipole model[99]
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图 8 TCTA: Bphen作为蓝色TADF EML以及TAPC: 3P-T2T作为橙色TADF EML的光学分析 (a)归一化发射光谱以及(b)在300 K下不同延迟时间下的归一化时间分辨光致发光光谱[113]
Figure 8. Optical analysis of tandem white OLED with TCTA: Bphen as blue TADF EML and TAPC: 3P-T2T as orange TADF EML (a) Normalized emission spectra and (b) normalized time-resolved photoluminescence spectra with different delay times (@300 K)[113]
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图 10 BRU+YRU混合叠层OLED白光器件能级结构及工作机理 (a) 能级结构示意图，以及蓝色TADF和黄色和红色磷光掺杂料的化学结构，(b)工作机制[122]
Figure 10. Energy level structure and working mechanism of BRU+YRU hybrid tandem OLEDs (a) Energy level structure, and structure formula of blue TADF, yellow and red phosphorescent dopants, (b) Working mechanism[122]
表 1 常见叠层OLED器件CGL的组成
Table 1 Composition of CGL for tandem white OLEDs
CGL结构及组成(n║p) Ref. LiF/Ca║Ag [43] LiF/Al║Ag [43] LiF/Al║Au [44] Al║WO3/Au [45] LiF/Al║WO3/Ag/MoO3 [46] LiF/Al║WO3/Au/MoO3 [47-48] LiF/Al║Dipyrazino[2, 3-f: 2', 3'-h]quinoxaline-2, 3, 6, 7, 10, 11-hex（HATCN） [49-50] LiF/Al║MoO3 [51-52] Lithium 8-Hydroxyquinolinolate（Liq）/Al║MoO3 [53] Zinc Phthalocyanine（ZnPc）║C60 [54] Copper(Ⅱ) phthalocyanine（CuPc）║C60 [54] Pentacene║C60 [55] Bathocuproine（BCP）: Cs║ITO [25, 56-57] BCP: Cs║V2O5 [58-60] BCP: Li║V2O5 [58, 61] Tris-(8-hydroxyquinoline)aluminum（Alq3）: Mg║WO3 [62-63] 4, 7-Diphenyl-1, 10-phenanthroline（BPhen）: Li║MoO3 [64] Alq3: Yb║WO3 [65] Alq3: Li║MoO3 [66] Alq3: Mg║WO3 [67] Alq3: Li║HATCN [68] 1, 3, 5-Tris(1-phenyl-1H-benzo[d]imidazol-2-yl)benzene（TPBI）: Li║NPB: FeCl3 [69] BPhen: Cs║NPB: 2, 3, 5, 6-Tetrafluoro-7, 7, 8, 8-tetracyanoquinodimethane（F4-TCNQ） [70] TPBI: Cs║4, 4', 4''-Tris(N-(aphthalene-2-yl)-N-phenylamino)triphenylamine（2-TNATA）: F4-TCNQ [71] BPhen: Rb2CO3║NPB: ReO3 [72] BPhen: Li║NPB: F4-TCNQ [68] BPhen: Cs2CO3║NPB: MoO3 [73] 
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