Research Progress in the Metal Oxide Heterojunction Photodetectors
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摘要: 金属氧化物（metal oxide，MO）因其具有易于制备、高稳定性、对载流子的选择性传输等优点，被广泛应用于光电探测领域。MO材料具有较强的光吸收，但表面效应和缺陷态等问题导致了MO光电探测器响应速度低和暗电流较大的问题。异质结中的内建电场可以有效促进光生电子-空穴对的分离，从而提升器件响应速度和降低器件暗电流。因此，构建金属氧化物异质结光电探测器（heterojunction photodetectors，HPDs），对于MO在光电子领域的进一步应用具有重要的意义。本文先介绍了MO的界面性质，然后围绕PN、PIN和同型异质结3种结构，对金属氧化物HPDs的工作机制进行了阐述。接着对响应波段在紫外-可见-近红外光区的、具有不同结构的MO/MO和MO/Si HPDs的性能参数进行了分析和比较，并讨论了金属氧化物HPDs的性能优化方法，最后对金属氧化物HPDs的发展进行了展望。Abstract: Metal oxides (MOs) have been widely used in photodetection because of advantages such as easy preparation, high stability, and selective transport of carriers. The MO materials exhibit strong light absorption properties. However, there are issues with MO photodetectors such as their low response speed and large dark current owing to the surface effects and defect states. The built-in electric field in the heterojunction can effectively promote the separation of photogenerated electron-hole pairs, thus improving the device response speed and reducing the dark current. Thus, the construction of metal oxide heterojunction photodetectors (HPDs) is of great significance for the further application of MO in the field of optoelectronics. This paper introduces the interface properties of MO and elaborates on the working mechanism of metal oxide HPDs around the PN, PIN, and isotype heterojunctions. Next, the performance parameters of MO/MO and MO/Si HPDs with different structure and response in UV-Vis-NIR band are analyzed and compared. Subsequently, improved methods of the metal oxide HPDs performances are discussed. Finally, the development of metal oxide HPDs is discussed.
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Keywords:
- photodetector /
- metal oxide /
- silicon /
- heterojunction
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0. 引言
快速反射镜由精密伺服控制相应的转角从而精确控制光束的偏转角度，它相对于普通的反射镜具有响应速度快、工作带宽高、指向精度高等优点[1-2]。伴随着航空光学成像、遥感技术等领域对成像分辨率的要求不断提高，大口径、长焦距光学系统在光电稳定平台获得广泛的应用。为满足大口径航空光学系统成像的高质量的要求，基于大口径快速反射镜温度-高度环境下的高精度跟瞄测试方法，大口径快速反射镜模拟框架粗稳伺服残差，测试光学系统精稳跟踪性能，能较真实地评估光学系统成像质量。如图 1所示。
其中对于大口径快速反射镜的控制方法决定了其稳定性能，间接地影响对光学系统成像质量的评价。其中文献[3]采用改进根轨迹方法去设计PID（proportional integral derivative）参量，保证良好的系统动态性能且改善系统机械谐振问题。但该方法需精准系统模型数学表达式。文献[4]采用改进自抗扰的方法，验证了低频信号下，该方法在原有自抗扰算法的基础上减小系统响应时间，提升系统跟踪精度。但缺乏工程化的理论保障。文献[5]采用基于内积的逆模型补偿方法可获得较好的控制效果，但当外界环境变化或受到干扰，则需要重新设计。
因此本文提出模糊自适应整定PID控制算法，在传统PID控制算法基础上，引入模糊理论和参数自整定方法，既具有模糊理论不完全依赖于精确模型，又能发挥出传统PID控制设计上简单、易于工程实现、鲁棒性好等特点。
1. ϕ500 mm大口径快速反射镜系统的结构与模型
1.1 大口径快速反射镜系统简介
本文以ϕ500 mm大口径快速反射镜系统为研究对象，由反射镜、音圈电机、控制器、线性功率放大器、电涡流位移传感器、反射镜基座等部分组成。该系统机械结构如图 2所示。
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图 2 ϕ500 mm大口径快速反射镜结构Figure 2. ϕ500 mm large aperture fast steering mirror mechanical structure该反射镜系统有X、Y两轴，每一个轴上装有两个音圈电机，反射镜安装在镜托上，镜托又通过“金字塔”状柔性铰链[6]（如图 3所示）与反射镜基座相连，同一轴上两个音圈电机直线运动推挽镜托，围绕旋转中心形成推挽力矩，推动“金字塔”状柔性铰链产生弹性位移，进而使得反射镜偏转。反馈测量元件为电涡流传感器，安装位置是与两轴成45°夹角。
其工作原理为当给定输入信号时，控制器根据相应控制算法将控制量输出到音圈电机驱动器上，同轴上的两个音圈电机获得大小相等且方向相反的驱动力，进而产生推挽驱动力矩，推动“金字塔”状柔性铰链在这个轴线方向上产生位移变化，通过电涡流传感器检测位移的变化（转换为转角），反馈给控制器，形成一个闭环控制系统。ϕ500 mm大口径快速反射镜的控制框图如图 4所示。
1.2 大口径快速反射镜系统的数学模型
ϕ500 mm大口径快速反射镜系统中包含有功率放大器、电涡流传感器、“金字塔”状柔性铰链和音圈电机等部分。其中功率放大器，传感器、柔性铰链等数学模型都可等效为一个比例模块，主要是音圈电机的数学模型的建立，将其等效为一个质量-阻尼-弹簧模型。如图 5所示。其中力学平衡方程方程为：
$$\begin{gathered} {F_{\rm{t}}} - kx - c\frac{{{\rm{d}}x}}{{{\rm{d}}t}} = m\frac{{{{\rm{d}}^2}x}}{{{\rm{d}}{t^2}}} \\ {F_{\rm{t}}} = {K_{\rm{t}}}I = BLI \\ \end{gathered} $$ (1) 电压平衡方程为：
$$u = L\frac{{{\rm{d}}i}}{{{\rm{d}}t}} + Ri + {K_{\rm{e}}}\frac{{{\rm{d}}x}}{{{\rm{d}}t}}$$ (2) 式中：F为作用在质量块m上的力；c为阻尼系数；I为电流；k为弹簧弹性系数；x为位移；L为电机电感；R为电机内阻；Kt为推力常数；Ke为反电动势系数。
根据各部分元器件的物理特性，建立大口径快速反射镜系统的数学模型如图 6所示。
各参数的定义如表 1所示。
表 1 数学模型的参数定义Table 1. The parameter definition of the mathematical modelSymbol Parameter L VCM inductance R VCM internal resistance Ke Back EMF coefficient Kt Force sensitivity Kc Flexible hinge elastic coefficient Ka Amplification coefficient M Load mass c System damping coefficient 根据图 6可以求得被控对象的传递函数为：
$$G(s) = \frac{{X(s)}}{{U(s)}} = \frac{{{K_{\rm{a}}}{K_{\rm{t}}}{K_{\rm{s}}}}}{{(M{s^2} + cs + k)(Ls + R) + {K_{\rm{t}}}{K_{\rm{e}}}s}}$$ (3) 式中：s为复频率。
但由于实际系统中，音圈电机的电感数值非常小，可以忽略不计。
本文采用白噪声为输入信号，偏转位移角度作为输出信号，采用Matlab中的系统辨识工具箱对大口径快速反射镜系统进行模型辨识，得到ϕ500 mm大口径快速反射镜系统的开环传递函数为：
$$G(s) = \frac{{X(s)}}{{U(s)}} = \frac{{5515.6}}{{{s^2} + 173.8s + 3473.2}}$$ (4) 式(4)就作为下面实验部分的被控对象的数学模型，对它展开实验仿真。
2. 模糊自适应整定PID控制
2.1 模糊理论
模糊理论可解决专家经验不易精确描述和不易定量表示等问题。其运用模糊数学理论和方法，用模糊数集表示规则的条件、操作，并把控制规则以及其他有关信息（如初始PID参数、评价指标等）作为知识库存入到计算机中，然后根据实际系统的响应情况，运用模糊理论推理，得到最佳输出参数，即可实现对最佳PID参数的自动调整[7]。
模糊控制器是把误差e和误差变化率ec作为输入，以满足任意时刻的e和ec对PID调节器中Kp、Ki、Kd 3个参数自整定的要求[8]，其控制器结构如图 7所示。
2.2 模糊控制
模糊控制的核心就是模糊控制器，具备下列3个功能：
① 将系统偏差从准确数字量转化为模糊量，即为模糊化过程。
② 由给定的对应模糊规则对模糊量进行模糊推理。
③ 将推理后的模糊输出量转化为精确量，即为反模糊化。
2.2.1 模糊化
模糊化是对精准数字量到模糊量的转换。模糊化过程中的模糊化函数一般用隶属度函数来表示。图 8有常用的3种模糊化函数。
设输入变量为e、ec，输出变量为Kp、Ki、Kd，且定义模糊变量对应的变化区间[-6, 6]，对应论域为：
$$ e、{e_{\rm{c}}}、{K_{\rm{p}}}、{K_{\rm{i}}}、{K_{\rm{d}}} = \{ - 6, - 4, - 2, 0, 2, 4, 6\} $$ 并设其模糊子集为：
$$ e、{e_{\rm{c}}}、{K_{\rm{p}}}、{K_{\rm{i}}}、{K_{\rm{d}}} = \{ {\rm{NB, NM, NS, Z, PS, PM, PB}}\} $$ 其中N、Z、P表示负、零、正。B、M、S表示大、中、小。输入、输出变量的隶属度曲线如图 9、图 10所示。
2.2.2 建立模糊控制规则表
知识库包含规则库和数据库。通常以规则表的形式表示模糊规则。
根据3个控制参数Kp、Ki、Kd在控制过程中的作用及其变化对控制系统产生不同的影响，得到模糊控制器中3个控制参数的自整定原则。
参考文献[9]中自整定原则并综合专家的控制经验，建立如下模糊逻辑语句。
1) If (e is NB) and (ec is NB) then (Kp is PB)(Ki is NB)(Kd is PS)
2) If (e is NB) and (ec is NM) then (Kp is PB)(Ki is NB)(Kd is NS)
3) If (e is NB) and (ec is NS) then (Kp is PM)(Ki is NM)( Kd is NB)
......
49) If (e is PB) and (ec is PB) then (Kp is NB)( Ki is PB)( Kd is PB)
由以上逻辑模糊语句可以得到以下Kp、Ki、Kd的模糊规则表（如表 2~表 4所示）。
表 2 Kp的模糊规则表Table 2. Fuzzy rules table of KpKp ec NB NM NS Z PS PM PB e NB PB PB PM PM PS Z Z NM PB PB PM PS PS Z NS NS PM PM PM PS Z NS NS Z PM PM PS Z NS NM NM PS PS PS Z NS NS NM NM PM PS Z NS NM NM NM NB PB Z Z NM NM NM NB NB 表 3 Ki 的模糊规则表Table 3. Fuzzy rules table of KiKi ec NB NM NS Z PS PM PB e NB NB NB NM NM NS Z Z NM NB NB NM NS NS Z Z NS NB PM NS NS Z PS PS Z NM NM NS Z PS PM PM PS NM NS Z PS PS PM PB PM Z Z PS PS PM PB PB PB Z Z PS PM PM PB PB 表 4 Kd的模糊规则表Table 4. Fuzzy rules table of KdKd ec NB NM NS Z PS PM PB e NB PS NS NB NB NB NM PS NM PS NS NB NM NM NS Z NS Z NS NM NM NS NS Z Z Z NS NS NS NS NS Z PS Z Z Z Z Z Z Z PM PB NS PS PS PS PS PB PB PB PM PM PM PS PS PB 2.2.3 反模糊化处理
反模糊化过程就是精确化。在模糊集合中取出代表模糊推理结果最大可能性的精确值。
综合考虑，最大隶属度函数法常用于对控制要求不高且计算相对简单的系统，而重心法的推理控制输出会更加平滑，因此本文采用重心法来进行反模糊化处理。得到Kp'、Ki'、Kd'的模糊控制调整参数，定义参数Kp、Ki、Kd调整算式如下：
$$\left\{ \begin{gathered} {K_{\rm{p}}}{\rm{ = }}{K_{{\rm{p0}}}} + \{ e, {e_{\rm{c}}}\} {K_{\rm{p}}}'{\rm{ = }}{K_{{\rm{p}}0}} + {\Delta _{\rm{p}}}{K_{\rm{p}}}' \\ {K_{\rm{i}}}{\rm{ = }}{K_{{\rm{i0}}}} + \{ e, {e_{\rm{c}}}\} {K_{\rm{i}}}' = {K_{{\rm{i}}0}} + {\Delta _{\rm{i}}}{K_{\rm{i}}}' \\ {K_{\rm{d}}}{\rm{ = }}{K_{{\rm{d}}0}} + \{ e, {e_{\rm{c}}}\} {K_{\rm{d}}}' = {K_{{\rm{d0}}}} + {\Delta _{\rm{d}}}{K_{\rm{d}}}' \\ \end{gathered} \right.$$ (5) 式中：$\Delta $p=1/5，$\Delta $i=1/10，$\Delta $d=1/10为量化因子，Kp0、Ki0、Kd0为初始参数。通过不断检测、计算e和ec，并传入模糊控制器中，得到3个输出参数Kp、Ki、Kd的调整量，实现对控制器参数的自适应调整[10]。
3. 系统仿真分析
在Matlab/Simulink中搭建整个系统模型如图 11所示，基于模糊控制与基于传统PID控制的ϕ500 mm大口径快速反射镜仿真模型。
传统的PID控制参数通过临界比例度法[11]整定控制参数，得到的3个参数Kp、Ki、Kd，同时显示阶跃输入信号（在某一时刻加干扰）、正弦信号，传统PID输出和模糊自适应PID输出如图 12、图 13所示，ϕ500 mm大口径快速反射镜系统的阶跃响应如图 14所示，正弦信号跟踪如图 15所示。
根据上图 12、图 13、图 14、图 15输出波形可分析得到，模糊自适应PID与传统PID控制相比，动态性能上调节时间更短，超调量也稍小，能够比较迅速地进入稳态，并且抗干扰的能力更强，能较快恢复到系统的稳定状态。具体数值如表 5所示。
表 5 控制性能对比Table 5. The comparison of control performanceController Control performance Transition process Overshoot/(%) Settling time/ms Raising time/ms Peaking time/ms Classic PID Dampled oscillation 7.10 112.0 35.5 81.0 Fuzzy PID Dampled oscillation 5.40 51.0 12.8 40.0 针对模糊控制部分，进一步观察模糊控制器的3个输出Kp、Ki、Kd的变化如图 16、图 17所示。
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图 16 阶跃信号下Kp、Ki、Kd的变化曲线Figure 16. Parameters Kp, Ki, Kd change curves under step signal![]()
图 17 正弦信号下Kp、Ki、Kd的变化曲线Figure 17. Parameters Kp, Ki, Kd change curves under sine signal由图 16、17分析可见，模糊控制器的3个输出参数随着系统调节发生变化，待系统趋于稳定后，参数不再发生变化且趋于稳定。对于正弦响应，系统稳态一直在变化，3个参数输出也一直处于类似正弦规律性变化。
4. 结论
与传统PID控制相比，本文提出的控制算法，系统上升时间缩短至12.8 ms左右，超调量减小了31%左右，达5.4%，调节时间提前2.2倍左右，达51 ms左右，而且抗干扰能力相对较强。均优于传统PID控制，明显改善大口径快速反射镜系统的动态、稳态性能，提高了响应速度，减小了跟踪误差。所以选用大口径快速反射镜去模拟框架的粗稳伺服残差进而检测光学成像系统的精稳跟踪性能。航空光学成像检测系统的精度能直接评价成像质量好坏。系统检测精度越高，就能对光学成像质量给出越高的评价，具有重大意义。
模糊控制算法的控制效果很大程度上依赖于专家控制经验，因此整个系统的动态、稳态性能受到专家控制经验的局限，但人工智能和机器学习的不断发展[12]，上述存在问题慢慢就会有新的解决途径。
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图 2 MO的界面特性：(a) 在MO界面上可以设计的关联电子的对称性和自由度[12]；(b) 传统半导体的相图（左）和界面电子行为（右）；(c)和(d)MO的相图（左）和界面电子行为（右），分别对应于产生电子形变(c)和不产生电子形变的情形(d)[13]
Figure 2. Properties of the MO interfaces: (a) The symmetries and degrees of freedom of correlated electrons that can be engineered at MO interfaces[12]; (b) Phase diagrams (left) and interface electronic behaviors (right) for conventional semiconductors; (c) and (d) Phase diagrams (left) and interface electronic behaviors (right) for MO, corresponding to the situations in which (c) and no electronic deformation is generated (d), respectively[13]
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图 3 金属氧化物HPDs的载流子传输机制：(a) MO/MO和(b)MO/Si PN结HPDs的能带示意图[23-24]；(c) MO/MO和(d) MO/Si PIN结HPDs能带示意图[25-26]；(e) MO/MO和(f) MO/Si同型异质结HPDs能带示意图[27-28]
Figure 3. Carrier transport mechanism of metal oxide HPDs: The energy band diagrams of (a) MO/MO and (b) MO/Si PN junction HPDs[23-24]; (c) MO/MO and (d) MO/Si PIN junction HPDs[25-26]; (e) MO/MO and (f) MO/Si isotype HPDs[27-28]
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图 5 薄膜型MO/MO HPDs的结构和性能：(a) n-Ga2O3/p-NiO HPD的结构[71]；(b) n-Ga2O3/p-NiO HPD的瞬态响应行为[71]；(c) WO3/TiO2 HPD的结构[28]；(d) TiO2，WO3和WO3/TiO2 PD I-V特性[28]；(e) 柔性ZnO/SrCoOx HPD的结构[40]；(f) ZnO/SrCoOx HPD在不同弯曲状态下的I-T曲线[40]
Figure 5. Structures and properties of the film-based MO/MO HPDs: (a) The structure of n-Ga2O3/p-NiO HPD[71]; (b) Transient response behavior of n-Ga2O3/p-NiO HPD[71]; (c) The structure of the WO3/TiO2 HPD[28]; (d) I-V characteristic of TiO2, WO3 and WO3/TiO2 PD[28]; (e) The structure of the flexible ZnO/SrCoOx HPD[40]; (f) I-T curves of the ZnO/SrCoOx HPD in different bending states[40]
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图 6 其他结构的MO/MO HPDs的性能：(a) ZnO/SnO2核壳纳米棒阵列HPD的结构[61]；(b) ZnO/SnO2核壳纳米棒阵列的横截面SEM图像[61]；(c) ZnO/SnO2 HPD在1 V偏压下的I-T曲线[61]；(d) NiO纳米片/ZnO纳米棒阵列HPD的结构[73]；(e) 零偏下的响应率在300~800 nm波长范围内的变化[73]；(f) 横杆NiO/SnO2纳米纤维阵列HPD的结构[35]；(g) NiO/SnO2 HPD的工作原理[35]；(h) NiO/SnO2HPD在－5 V偏压下的响应率和探测率曲线[35]；(i) α‑Ga2O3纳米棒阵列/Cu2O纳米球HPD的结构及其测试系统[39]
Figure 6. Properties of MO/MO HPDs with other structures: (a) The structure of ZnO/SnO2 core-shell nanorods array HPD[61]; (b) Cross-sectional SEM images of ZnO/SnO2 core-shell nanorods array[61]; (c) I-T curves of ZnO/SnO2 HPD at 1V bias[61]; (d) Structure of NiO nanosheet/ZnO nanorods array HPD[73]; (e) Wavelength-dependent responsivity at zero bias ranging from 300-800 nm[73]; (f) Structure of cross-bar NiO/SnO2 nanofiber array HPD[35]; (g) Working principle of the NiO/SnO2 HPD[35]; (h) Responsivity and detectivity curves of NiO/SnO2 HPD at -5 V bias[35]; (i) Structure and testing system of α‑Ga2O3 nanorods array/Cu2O nanosphere HPD[39]
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图 7 MO/Si HPDs的结构和性能：(a) p-NiO/n-Si HPD的结构[76]；(b) p-NiO/n-Si HPD I-V特性，插图显示了I-V曲线的局部放大图[76]；(c) n-ZnO纳米管/p-Si HPD的结构[55]；(d) n-ZnO/p-Si HPD的响应率和探测率曲线[55]；(e) r-GO/n-Si HPD的结构[51]；(f) r-GO/n-Si HPD的瞬态响应[51]
Figure 7. Structures and properties of MO/Si HPDs: (a) Structure of p-NiO/n-Si HPD[76]; (b) I-V characteristic curves of the p-NiO/n-Si HPD, the inset shows a close-up view of the I-V curves[76]; (c) Structure of the n-ZnO nanotubes/p-Si HPD[55]; (d) The responsivity and detectivity curves of the n-ZnO/p-Si HPD[55]; (e) Structure of the r-GO/n-Si HPD[51]; (f)Transient response of the r-GO/n-Si HPD[51]
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图 8 金属氧化物HPDs的性能优化：(a) ε-Ga2O3/p-Si和ε-Ga2O3/Al2O3/p-Si HPDs的log I-V曲线[26]；(b) NiO/ZnO和NiO/TiO2/ZnO HPDs在暗条件的log I-V曲线[67]；(c) MoO3－x/Si HPD在光照下的J−V曲线[48]；(d) p-NiO/n-ZnO和Pd NPs/p-NiO/n-ZnO NWs在可调制紫外光下的光电流和暗电流比[84]；(e) Pd NPs/p-NiO/n-ZnO NWs的能带图[84]；(f) 采用Al六方点等离子体阵列制备的Pd/TiO2/p-Si/Al HPD[87]；(g) NiOx/n-Si和p-Ag: NiOx/n-Si HPDs的响应光谱[88]；(h) 不同Eu掺杂浓度的TiO2薄膜的Tauc图[89]；(i) n-β-Ga2O3/p-MnO QD和n-β-Ga2O3器件的响应光谱[37]
Figure 8. Performance optimization of metal oxide HPDs (a) Log I-V curves of ε-Ga2O3/p-Si and ε-Ga2O3/Al2O3/p-Si HPDs[26]; (b) Log I-V curves of NiO/ZnO and NiO/TiO2/ZnO HPDs in the dark[67]; (c) J-V curves of MoO3−x/Si HPD under illumination[48]; (d) Iphoto/Idark ratios for the p-NiO/n-ZnO and Pd NPs/p-NiO/n-ZnO NWs unde modulated UV illumination[84]; (e) Band diagrams for Pd NPs/p-NiO/n-ZnO NWs[84]; (f) Design of the fabricated Pd/TiO2/p-Si/Al HPD with Al hexagonal dots plasmonic array[87]; (g) Response spectra of NiOx/n-Si and p-Ag: NiOx/n-Si HPDs[88]; (h) Tauc plots of Eu: TiO2 films with different doping concentration[89]; (i) Response spectra of n-β-Ga2O3/p-MnO QDs and n-β-Ga2O3 device[37]
表 1 不同MOs的性质
Table 1 Properties of different MOs
MOs Conduction type Eg/(eV) Exciton binding energy/(meV) Crystal system Space group Ref. ZnO(Wurtzite) n 3.3 60 Hexagonal P63mc [2] TiO2(Anatase) n 3.2 130 Tetragonal C4/amc [3] TiO2(Rutile) n 3.0 130 Tetragonal P42/mmm [3] β-Ga2O3 n 4.9 40-50 Monoclinic C2/m [4] SnO2 n 3.6 130 Tetragonal P42/mnm [5] MoO3 p 3.0 - Orthorhombic - [6] V2O5 P 2.3 - Monoclinic P21/C [7] NiO P 3.6 110 Cubic Fm3m [8] 
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表 2 MO/MO和MO/Si HPDs的性能参数对比
Table 2 Comparisons of performance parameters for MO/MO and MO/Si HPDs
Year Structure Fabrication method Bias/V λ/nm EQE/(%) R/(AW−1） D*/Jones Rectification ratio Rise/fall time Ref. 2012 α-Fe2O3/p-Si Chemical solution deposition - 403 - 2×103 - - < 1 ms [43] 2012 TiO2/SrTiO3 Sol-gel 10 260 - 46.1 - - 3.5 ms/1.4 s [44] 2014 NiO/ZnO Spin coating -1 350 1800 10.2 4.66×1012 5×102 - [45] 2015 CuO/SnO2 Magnetron sputtering 0.2 290 - 10.3 - - - [46] 2015 MgZnO/i-MgO/p-Si MOCVD 6 240 600 1.16 - - 15 μs [47] 2015 MoO3−x/n-Si Thermal evaporation 0 900 - - 6.29×1012 - 1/51.4 μs [48] 2016 β-Ga2O3/p-Si Pulsed laser deposition 3 254 1.8×105 370 - - 1.79/0.27 s [49] 2016 Mg0.18Zn0.82O/p-Si Magnetron sputtering - 320 - 4.21 - 32500 - [50] 2016 GO/n-Si Modified Hummers method - 600 - 1.52 - - 2/3.7 ms [51] 2017 NiO/n-Si Magnetron sputtering 5 365 - 4.5 - - 266/200 ms [52] 2017 SnO2/SiO2/p-Si Magnetron sputtering -1 365 - 0.355 2.66×1012 - < 0.1 s [53] 2017 TiO2/NiO Sol-gel
spin-coating and oxidation6 280 80500 181.9 1.56×1014 - 717/598 ms [54] 2017 p-Si/n-ZnO NTs Pulsed laser deposition -5 365 - 101.2 - - 0.44/0.59 s [55] 2019 n-TiO2/p-Si Thermal oxidation -4 365 - 6.74 1.31×1012 - 127.6 /120.3 μs [56] 2019 V2O5/n-Si Thermal evaporation - 940 - 0.185 1.34×1012 - 9.5/123 μs [57] 2019 ZnO/NiO Electrospinning 0 350 - 0.415×10−3 - - 7.5/4.8 s [58] 2019 n-SnO2/SiNWs Metal assisted chemical etching 5 UV - 0.35 8.03×1012 172.3 - [59] 2020 NiO/β-Ga2O3 Magnetron sputtering 10 245 - 27.43 3.14×1012 - - [60] 2020 ZnO/SnO2 core-shell NAs Chemical liquid deposition 1 365 - 28.5(± 0.6) 2.9× 1014 - 8.7 s/20.8 s [61] 2020 p-Cu2O/n-Si Successive ionic layer
adsorption and reaction-5 500 3780 16.2 1.78×1012(−0.1 V) 118.4 < 10 ms [62] 2021 MoO3−x/Al2O3/n-Si Thermal evaporation -5 980 900 7.11 9.85×1012 - 0.109/0.69 ms [63] 2022 NiO/IGZO Magnetron sputtering 0 365 - 0.0288 6.99×1011 7.4×104 15/31 ms [64] 2022 p-Mn2O3/n-Si Rapid thermal
oxidation- 500 140 0.5 7.2×1012 - - [65] 2022 p-Ag2O/n-Si Rapid thermal
oxidation-4 450 118 0.43 9×1011 - - [66] 2023 NiO/TiO2/ZnO Magnetron sputtering 2 365 - 291 6.9×1011 104 163/282 ms [67] 2023 p-NiO/SiO2/n-ZnO Magnetron sputtering 2 365 2×103 5.77 1.51×1011 57 48 ms [68] 2023 p-NiO/n-ZnO/n-Si Magnetron sputtering -1 280 - 3.672 3.3×1012 - 10.5/0.4 s [69]
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