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
红外图像处理技术广泛应用于军事、医疗、防灾等领域。其主要应用是提高红外成像设备的成像质量，处理现有的红外图像。红外图像的成像特征来自目标的能量辐射，其特点是能量不均匀，分辨率对比度较低，亮度分布不均[1]。因此，红外图像对比度增强的方法在实际应用中具有重要意义[2]。
图像的对比度增强是图像信号处理中流行的一个研究方向。其主要目的是扩大图像对比度，提高图像的视觉效果，反映一些隐藏在图像中的细节[3]。目前常见的对比度增强方法是针对可见光图像进行增强，大致分为两类：①直接增强输入图像[4]。②通过间接的方法增强输入图像[5]。通常直接增强很容易对噪声进行过度的增强。间接增强法则利用输入灰度直方图的统计量，对灰度直方图进行处理，最后通过处理后的直方图建立输入图像的灰度与输出图像的灰度之间的映射关系，以增强图像的对比度。
基于直方图均衡化的图像对比度增强方法，在可见光图像处理领域得到了广泛的应用，在红外图像对比度增强领域也具有较好的效果。对于给定的图像，该方法使用图像的累积分布函数来扩展图像的动态范围，以提高图像的对比度[6]。然而不同灰度的增强程度与其直方图的分布有关，因此直方图均衡化的方法容易产生灰度平均位移的问题，导致图像目标或噪声被严重衰减或者过度增强[7]。多位学者对基于直方图均衡化的方法进行了研究。Hum Y. C.等人使用全局加局部的对比度增强算法，增强图像的低对比度区域，抑制高对比度区域[8]。J. Bae等人先根据对数域的直方图将图像分成K类，然后每一个类使用不同的log域映射算法[9]。Weitao Deng等人通过递归将图像的直方图分成多个子直方图，通过特定的方式加权平均得到均分分布的直方图，最后根据原始的累计直方图将两种直方图加权得到最终用于直方图均衡的直方图[10]。T. Arici等人提出多种直方图修改方案，根据其特征提出了一种新的基于局部对比度的直方图统计方法，在小计算量下达到较好的效果[11]。
本文提出一种基于Sobel梯度的直方图均衡算法，可以获得更好的图像增强效果，同时通过双Gamma映射调整映射曲线，可有效避免梯度过大导致的亮区过曝、暗区欠曝的现象。
1. 提出的算法
1.1 直方图均衡化
红外探测器的输出数据一般为16 bit的高动态范围图像，通过对16 bit图像进行直方图均衡化，可以得到8 bit的灰度图像，同时实现了对红外图像的动态范围压缩和增强[12]。假设红外探测器输出的某一帧16 bit图像共有L(1≤L≤216)个灰度等级，则图像的直方图可定义如下：
$$\operatorname{hist}(l)=n(l), \quad l_{\min } \leqslant l \leqslant l_{\max }$$ (1) 式中：n(l)表示灰阶为l的像素点在全图出现的次数；lmin为全图最小灰阶；lmax为全图最大灰阶。图像直方图描述了所有灰度级在全图范围内的分布情况，直方图均衡化的核心思想是通过一种特定的映射关系将原图直方图分布中较为集中的像素映射成均匀分布[13]。
将直方图正则化后，可得到图像的离散概率密度函数，定义如下：
$$ p\left( l \right) = \frac{{{\rm{hist}}\left( l \right)}}{N} $$ (2) 式中：p(l)表示灰阶为l的像素点在全图出现的概率；N为全图像素总个数。由此，直方图均衡化过程可描述如下：
$$p^{\prime}=T(p)$$ (3) 式中：p′表示直方图均衡化后的离散概率密度函数，为均匀分布，即所有灰阶出现的概率相同。通过求解上式，可得到映射关系T为累计分布函数，可定义如下：
$$ c\left( l \right) = \sum\limits_{i = {l_{\min }}}^l {p\left( i \right)} $$ (4) 式中：c(l)∈[0, 1]，将其乘以8 bit最大灰度值255后即可得到16 bit图像到8 bit图像的映射关系。对于16 bit图像上某一个灰度值为l的像素，映射后其在8 bit图像上的对比度可描述如下：
$$ \Delta l = 255 \times \left( {c\left( l \right) - c\left( {l - 1} \right)} \right) = 255 \times p\left( l \right) $$ (5) 从上式可以看出，某一个灰阶在8 bit图像的亮度及对比度与其在16 bit上出现的概率有关。其出现概率越高，映射后与相邻灰度级所占用的8 bit动态范围越大，表现为图像的对比度越大。当某个范围内的灰阶在全图所占比例很大时，映射后会占用8 bit图像很大的动态范围，从而压缩其余部分所能占用的动态范围。但占比很大的像素并不一定会存在很多细节，从而导致占比少、细节多的部分图像细节被严重压缩。因此本文提出一种基于Sobel梯度的直方图均衡算法，可以自适应地为图像强梯度的灰阶分配高对比度，能有效地保留并增强原图的细节。针对梯度累计过大导致的梯度直方图峰值过高，从而出现的对比度分配过度现象，使用一种双Gamma映射对最终的映射曲线进行调整，可有效地抑制图像亮部的过曝现象，同时提高暗部的细节。
1.2 梯度直方图均衡化
对于输入的16 bit图像srcImage，首先计算其Sobel梯度图像gradImage，梯度图像中每一个点计算公式如下：
$$ \begin{array}{l} gx\left( {i, j} \right) = \left( {{\rm{srcImage}}\left( {i + 1, j - 1} \right) + 2 \times {\rm{srcImage}}\left( {i + 1, j} \right) + {\rm{srcImage}}\left( {i + 1, j + 1} \right)} \right) - \hfill \\ \quad \quad \quad \, \, \, \, \,\,\,\, \left( {{\rm{srcImage}}\left( {i - 1, j - 1} \right) + 2 \times {\rm{srcImage}}\left( {i - 1, j} \right) + {\rm{srcImage}}\left( {i - 1, j + 1} \right)} \right) \hfill \end{array} $$ (6) $$ \begin{array}{l} gy\left( {i, j} \right) = \left( {{\rm{srcImage}}\left( {i + 1, j + 1} \right) + 2 \times {\rm{srcImage}}\left( {i, j + 1} \right) + {\rm{srcImage}}\left( {i - 1, j + 1} \right)} \right) - \hfill \\ \quad \quad \quad \, \, \, \, \,\,\,\, \left( {{\rm{srcImage}}\left( {i + 1, j - 1} \right) + 2 \times {\rm{srcImage}}\left( {i, j - 1} \right) + {\rm{srcImage}}\left( {i - 1, j - 1} \right)} \right) \hfill \end{array} $$ (7) $$ {\rm{gradImage}}\left( {i, j} \right) = \sqrt {gx{{\left( {i, j} \right)}^2} + gy{{\left( {i, j} \right)}^2}} $$ (8) 式中：(i, j)为图像坐标，对梯度图像进行归一化：
$$ {\rm{gradImageNor}}\left( {i, j} \right) = {\rm{gradImage}}\left( {i, j} \right)/\max \left( {{\rm{gradImage}}} \right) $$ (9) 式中：max(gradImage)为梯度图像最大值。
红外图像在经过非均匀性校正后依然会存在一定程度的非均匀性噪声，特别是在局部动态范围很低的背景上表现的尤为明显。非均匀性噪声往往会产生很小的梯度值，但当噪声数量较多时，对小梯度进行累计，依然会在梯度直方图上产生较高的bin值，因此需要在梯度统计时进行筛选，避免为非均匀性噪声产生较高的bin值。本文通过设置固定的阈值gradTh对梯度进行筛选，将小于gradTh的梯度值赋值为0，防止后续统计直方图时对其进行累计。
$$ \begin{array}{l} {\rm{gradImageTh}}\left( {i, j} \right) = \hfill \\ \left\{ {\begin{array}{*{20}{c}} 0&{{\rm{gradImageNor}}\left( {i, j} \right) < {\rm{gradTh}}} \\ {{\rm{gradImageNor}}\left( {i, j} \right)}&{{\rm{gradImageNor}}\left( {i, j} \right) \geqslant {\rm{gradTh}}} \end{array}} \right. \hfill \end{array} $$ (10) 在计算梯度图像后，需要计算输入图像的梯度直方图，计算过程与正常直方图统计类似，只是将灰度级出现的个数累计更改为对应位置的梯度累计，16 bit图像中某个灰度级k的梯度直方图bin值histGrad(k)计算公式如下：
$$ {\rm{histGrad}}\left( k \right) = \sum\limits_{i = 1}^h {\sum\limits_{j = 1}^w {f\left( {i, j, k} \right) \times g\left( {i, j} \right)} } $$ (11) 式中：h、w分别为图像高和宽；k表示16 bit图像中出现的灰度级，lmin≤k≤lmax。当srcImage(i, j)＝k时，f(i, j, k)＝1，否则f(i, j, k)＝0。对梯度直方图累计，得到其累计直方图，计算公式如下：
$$ {\rm{histCum}}\left( k \right) = \sum\limits_{l = {l_{\min }}}^k {{\rm{histGrad}}\left( l \right)} $$ (12) 对累计直方图归一化即可得到最终的映射曲线：
$$ {\rm{map}}\left( i \right) = \frac{{{\rm{histCum}}\left( i \right)}}{{\max \left( {{\rm{histCum}}} \right)}} $$ (13) 式中：lmin≤k≤lmax，max(histCum)表示累计直方图最大值，即histCum(lmax)。同理，将其乘以8 bit最大灰度值255后即可得到16 bit图像到8 bit图像的映射关系。
将普通直方图和梯度直方图均归一化后，在某一场景下对比如图 1所示，其中(a)为原始图像归一化到0~1后直接显示，(b)为该图像的直方图和梯度直方图。(b)中红色虚线框区域表示天空所处灰度级范围，绿色虚线框区域表示树木所处灰度级范围。在原图中，由于天空和树木占比很大，所以直方图中在天空所处的灰度级和树木所处的灰度级范围产生了很高的bin（指直方图中横坐标的某一个范围，其高度代表图像灰阶中处于该范围的像素点个数，本文中横坐标范围固定为1个灰阶），其余部分的bin很低。而在梯度直方图中，由于天空部分没有很高的梯度值，所以bin被拉低，而树木存在较强的梯度值高bin得到了保留，图像中建筑等其余部分由于存在很高的梯度，bin得到了拉伸。
1.3 双Gamma映射
红外图像的观察者通常更加关注温度明显异于环境温度的目标，如导弹尾焰、猎物、房屋漏水处等，这些目标在16 bit数据中的灰阶分布一般都处于全图灰阶的最大值或最小值附近，即对应为图像的亮区和暗区。梯度直方图在进行梯度累计时，直方图bin的高度不仅与梯度值相关，还与bin对应的灰度级在图像中的个数有关。图像背景一般会占到图像绝大部分，导致背景灰度等级对应的bin会产生较大的值，从而导致映射后背景会占用很大的8 bit动态范围。而在图像的亮区和暗区即使存在很大的梯度，由于像素个数较少，其梯度直方图的bin依然不会很高，且8 bit动态范围被背景占用了很大一部分，导致映射后的图像亮区和暗区可以使用的动态范围很小，从而出现亮区过曝、暗区欠曝的现象。如图 2所示，场景中人物头部基本都发生了过曝。
针对上述问题，本文引入一种双Gamma映射曲线[14]对最终得到的map进行二次映射。修正后的映射曲线可计算如下：
$$ {\rm{ma}}{{\rm{p}}_{{\rm{cor}}}}\left( i \right) = \frac{{{\rm{map}}{{\left( i \right)}^{1/{\rm{gamma}}}} + \left( {1 - {{\left( {1 - {\rm{map}}\left( i \right)} \right)}^{1/{\rm{gamma}}}}} \right)}}{2} $$ (14) 式中：gamma为手动设置的值，可根据场景手动调整，不同gamma值下的反S型曲线如图 3所示。当gamma＝1时，相当于对原始曲线不做映射。随着gamma值的增大曲线两端的斜率会增加，曲线中部的斜率会降低，意味着图像亮区和暗区的对比度被拉伸，图像灰度级中间区域对比度被压缩。
2. 实验与结果分析
为验证本文提出算法的有效性，在非制冷1280×1024分辨率探测器和制冷640×512分辨率探测器采集的raw数据上进行了仿真验证。其中非制冷1280×1024分辨率探测器的raw数据在进行动态范围压缩前进行了非均匀性校正和去横竖纹处理，制冷640×512分辨率探测器的raw数据进行了非均匀性校正和去噪处理。
2.1 梯度直方图均衡化效果分析
如图 4所示，对比了室外场景下直方图均衡化和梯度直方图均衡化的效果，以及两种算法获得的映射曲线。图(a)为直方图均衡化效果，图(b)为梯度直方图均衡化效果，图(c)为两种算法获得的映射曲线。其中图(c)中红色虚线框表示直方图均衡化映射曲线对应图像中红色区域天空部分，绿色虚线框表示直方图均衡化映射曲线对应图像中绿色区域建筑部分。可以看到在该场景中，由于天空占比比较大，因此原本在16 bit图像中动态范围很小的天空部分，在经过直方图均衡化算法后，在8 bit图像中占用了很大的动态范围内。从而导致天空部分对比度被过度拉伸，增强了天空部分的噪声。另一方面，由于天空部分占用了很多动态范围，原本在16 bit图像中动态范围很大的建筑部分可以被分配的动态范围变得很少，导致建筑部分对比度很低，部分细节无法被凸显。由于梯度直方图根据像素梯度进行8 bit图像动态范围的分配，天空部分梯度值很小，从而在8 bit图像中被分配了很少的动态范围，抑制了噪声；建筑部分梯度值大，从而在8 bit图像中被分配了很大的动态范围，凸显了建筑细节。
为量化分析对比度的分配情况，本文定义对比度计算公式如下：
$$ c = \sum\limits_\delta ^{} {{\delta ^2} \times {P_\delta }} $$ (15) 式中：δ表示图像中任意相邻两点的灰度差，本文计算时取四领域，即只计算水平方向和垂直方向相邻的像素；Pδ表示灰度差为δ在整个统计过程中出现的概率。如表 1所示，计算了图 4中红色框区域天空部分和绿色框区域建筑部分的局部对比度以及全图对比度。可以看出梯度直方图均衡化将非显著性区域的天空部分对比度从原有的3.74压制到了0.19，而为显著性区域的建筑部分分配了更高的对比度，从之前的54.33提升到了123.96，同时全图的对比度也有明显增强。
表 1 两种算法对比度对比Table 1. Comparison of HE with GHEAlgorithms Sky Architecture Picture HE 3.74 54.33 74.14 GHE 0.19 123.96 98.19 2.2 噪声抑制效果分析
如图 5所示，对比了室内某一场景下的不进行噪声抑制和增加噪声抑制的效果及映射曲线，其中阈值gradTh＝0.05。图(a)为直方图均衡化效果，图(b)为梯度直方图均衡化效果，图(c)为噪声抑制的梯度直方图均衡化效果，图(d)为3种算法获得的映射曲线。其中图(d)中红色虚线框表示直方图均衡化映射曲线对应图像中背景区域。可以看到在该场景下，原本的直方图均衡化和梯度直方图均衡化均会放大背景的噪声。其主要原因为背景噪声的梯度值虽然很小，但数量很多，经过累计后背景所在的灰度级依然会在直方图均衡上产生很高的bin，导致其在8 bit图像中占用很大的动态范围，从而增强噪声。但增加阈值限制后，背景所在灰度级梯度值不会被累计，从而避免该问题，同时增强目标的对比度。但同时与背景噪声灰度级接近的细节也会被抑制，需要根据场景适当调整该参数，图中gradTh＝0.05。
2.3 双Gamma映射效果分析
如图 6和图 7所示，分别对比了室外和室内场景下的不进行双Gamma映射和增加双Gamma映射的效果及映射曲线，其中gamma＝2.2。图(a)为直方图均衡化效果，图(b)为梯度直方图均衡化效果，图(c)为双Gamma映射的梯度直方图均衡化效果，图(d)为3种算法获得的映射曲线。图 6中(a)~(c)红色框选区域与(d)红色虚线框选的直方图均衡化映射曲线相对应。可以看到在该场景下，直方图均衡化和梯度直方图均衡化均在红色框选区域内产生了很严重的过曝，主要由于原图中的树木和远处建筑所处灰度等级占全图像素的绝大部分，且该部分灰阶也具备一定的梯度值，进行梯度累计后产生很高的bin，导致直方图均衡和梯度直方图均衡都会为该部分在8 bit图像上分配很大的动态范围，从而压缩了其他部分可用的动态范围，导致反射太阳的楼体出现过曝，看不到窗户的细节。增加双Gamma映射后，16 bit图像动态范围的中间部分映射后的动态范围被压缩，过曝区域映射后的动态范围被拉伸，使得窗户的细节被保留。
同样的，图 7中(a)~(c)红色框选区域与(d)红色虚线框选的双Gamma映射的梯度直方图均衡化映射曲线相对应。该场景下直方图均衡化和梯度直方图均衡化效果，人物头部都发生了过曝，细节损失严重，增加双Gamma映射后，人物头部的映射曲线会更加陡峭，意味着分配了更高的对比度，从而使得人物头部细节凸显。
3. 结语
本文提出一种基于Sobel梯度的直方图均衡化算法，可以根据全图的梯度分布情况，自适应地为强梯度的灰阶分配高对比度。同时使用一种双Gamma映射对最终的映射曲线进行调整，使得红外图像动态范围压缩过程中，不会出现明显的过曝和欠曝现象，能更好地保留亮区和暗区的细节。
但本文提出的算法仍有一定的局限性。算法在自适应为强梯度的灰阶分配高对比度的同时，不可避免会导致图像弱纹理的对比度被压缩，从而导致一定的细节丢失。另外，在低动态范围场景，动态范围压缩的过程逆转为动态范围增强，使用本文算法较容易出现图像失真现象。
在后续的研究中，可以尝试将图像的梯度信息和其他信息（场景动态范围、局部空间熵等）进行融合，从多个维度考虑对比度的自适应分配问题，避免出现图像失真。同时，也可以考虑结合局部对比度增强的方法，避免弱纹理被压缩的问题。
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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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