Research Progress in Ultraviolet Enhanced Image Sensors
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摘要: 近年来图像传感器在紫外成像的应用越来越广泛，尤其是以CCD（charge coupled device）和CMOS（complementary metal oxide semiconductor）为主的紫外图像传感器受到了研究人员的广泛关注。半导体技术的进步和纳米材料的发展进一步推动了紫外图像传感器的研究。本文综述了国内外紫外增强图像传感器的研究进展，介绍了几种增强器件紫外响应的材料，另外还简要概述了紫外图像传感器在生化分析、大气监测、天文探测等方面的应用，并讨论了CCD/CMOS图像传感器在紫外探测方面所面临的挑战。Abstract: In recent years, image sensors are more and more widely used in ultraviolet imaging, especially the ultraviolet image sensors based on CCD and CMOS have attracted intensive attention of researchers. The progress of semiconductor technology and the development of nanomaterials further promote the research of ultraviolet image sensor. In this review, the research progress of ultraviolet enhanced image sensor at home and abroad is reviewed, and several materials enhancing the ultraviolet response of the device are introduced. In addition, the applications of ultraviolet image sensor in biochemical analysis, atmospheric monitoring and astronomical detection are briefly summarized, and the challenges faced by CCD/CMOS image sensors in ultraviolet detection are discussed.
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Keywords:
- ultraviolet enhancement /
- CMOS image sensor /
- CCD
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0. 引言
湿法烟气脱硫利用石灰石浆液吸收烟气中的SO2，具有高效率和高可靠性等优势，已成为当前火电厂和化工厂脱硫的主力技术[1]。但是火电厂运行过程中，由于煤燃烧和SO2氧化，特别是在选择性催化还原脱销系统中催化剂作用下，SO2会更有利于氧化成生成SO3，造成烟气中SO3浓度显著增加[2-3]。烟气进入脱硫塔系统后，温度迅速冷却至酸露点以下，形成细小SO3酸雾，而单脱硫塔的SO3脱除效率仅为30%~40%[4]。
烟气中的SO3、SO2，HF及其它酸性物质会导致塔体金属发生化学腐蚀，脱硫塔内还存在电化学腐蚀、磨蚀、结晶腐蚀、垢下腐蚀和氯离子腐蚀[5]。在以上多种方式的共同作用下，受到内涂玻璃鳞片、聚烯烃共聚物、改性聚脲或纳米复合涂料等物质防护的脱硫塔仍可能发生腐蚀。
目前，对脱硫塔的检测主要有外观损伤、钢材厚度、力学性能、焊缝缺陷检测和构件变形等项目[6]。以上检测项目能够有效对停机后的脱硫塔健康状态进行评测。但是，对运行中的脱硫塔腐蚀状态进行有效检测未见文献报道。结合脱硫塔运行参数和结构参数，本文提出了采用传热学反演的方法根据表面红外热像进行运行中脱硫塔壁厚定量检测，并以某厂的烟气脱硫塔为对象进行了实验验证。
1. 脱硫塔传热模型
研究对象为图 1所示的脱硫塔，其内部环境复杂，无法布置有效的温度测量装置；脱硫过程是一个包含了传热、传质和化学反应的复杂过程，难以准确地用数学语言描述。为了建立脱硫塔的传热模型，本文进行以下简化：
1）脱硫塔为轴对称结构，内壁面热流沿周向分布均匀；
2）脱硫塔内部有玻璃鳞片防腐涂层，涂层质地均匀且热物性参数为各向同性；
3）烟气和石灰浆液对脱硫塔的传热，可等效为对脱硫塔内壁施加有沿轴向分布的加热热流；
4）忽略脱硫塔内部喷嘴和支撑结构对脱硫塔壁面温度分布的影响；
5）忽略脱硫塔内壁和防腐涂层的接触热阻；
6）忽略脱硫塔的轴向导热；
7）脱硫塔的温度场为稳态。
脱硫塔浆液区是腐蚀的重灾区，本文以浆液区段为研究对象，简化后的脱硫塔浆液区如图 2所示，具体几何参数及热物性参数见表 1。脱硫塔内壁s1存在轴向分布热流，外表面s2与环境存在对流换热，且s2面上的温度可以直接测量，其他表面s3绝热。记x＝(ϕ, θ, z)为空间坐标向量，脱硫塔温度场的控制方程为：
表 1 脱硫塔几何参数及热物性参数Table 1. Geometric parameters and thermophysical parameters ofdesulfurization towerMaterial Thickness /
mmDiameter/
mmThermal conductivity/
[W/(m·K)]Anticorrosive
coating4 5520 0.35 The tower wall 14 5528 48.85 $$\frac{1}{r}\frac{\partial }{{\partial r}}(\lambda (\mathit{\boldsymbol{x}})r\frac{{\partial T(\mathit{\boldsymbol{x}})}}{{\partial r}}) + \frac{1}{{{r^2}}}\frac{\partial }{{\partial \varphi }}(\lambda (\mathit{\boldsymbol{x}})\frac{{\partial T(\mathit{\boldsymbol{x}})}}{{\partial \varphi }}) + \frac{\partial }{{\partial z}}(\lambda (\mathit{\boldsymbol{x}})\frac{{\partial T(\mathit{\boldsymbol{x}})}}{{\partial z}}) = 0$$ (1) 边界条件为：
$$ - \lambda (\mathit{\pmb{x}})\frac{{\partial T(\mathit{\pmb{x}})}}{{\partial n}} = q(z)\;\quad \mathit{\pmb{x}} \in {s_1}$$ (2) $$ - \lambda (\mathit{\pmb{x}})\frac{{\partial T(\mathit{\pmb{x}})}}{{\partial n}} = h(T(\mathit{\pmb{x}}) - {T_f})\;\quad \mathit{\pmb{x}} \in {s_2}$$ (3) $$ - \lambda ({\mathit{\pmb{x}}})\frac{{\partial T({\mathit{\pmb{x}}})}}{{\partial n}} = 0\;\;\;\;\;{\mathit{\pmb{x}}} \in {s_3}$$ (4) 式中：q(z)为内壁沿轴向z的热流密度分布；h为表面s2的对流换热系数；Tf为环境温度；n为表面的外法线方向。
若已知脱硫塔的几何结构和热边界条件，利用有限元法（finite element method, FEM）求解公式(1)~(4)，可以确定脱硫塔的温度场T(x)，作为壁厚检测的基础。
2. 基于反问题的脱硫塔壁厚检测
运行中脱硫塔壁厚d是影响脱硫塔表面温度分布的关键因素之一，如果脱硫塔壁面腐蚀减薄，则传热热阻减小，在腐蚀部位对应的塔外表面形成局部高温区域，如图 1(b)所示。因此，可通过求解导热反问题根据脱硫塔表面红外热像进行脱硫塔壁厚检测。
2.1 壁厚检测方案
在如图 2所示的脱硫塔表面热像图中，选取高温区域的K个温度测点，依据此测量信息Tkmea（k＝1, 2, …, K）采用共轭梯度方法（conjugate gradient method，CGM）求解多变量稳态传热反问题[7-8]，进行壁厚d的反演；由于脱硫塔为薄壁结构，热扩散效应较弱，则外表面高温区域可看作与内壁腐蚀区域接近。为了便于问题讨论，在本文中，内壁腐蚀区域用圆柱近似。
然而，壁厚反演过程中正问题的计算需要已知脱硫塔内壁热边界条件如热流q(z)。而内壁热流q(z)难以直接测量。如果直接同时反演壁厚和内壁热流q(z)，可能因为测量信息不能够同时对壁厚和热流具有较大的灵敏度，造成检测系统的病态程度加剧。
因此，检测方案包括了两步：先进行内壁热流定量识别，再定量识别壁厚。内壁热流可采用CGM反演得到：在脱硫塔外表面高温区域附近同等高度的温度正常区域沿周向选取M个测点，以该测点的温度信息Tmmea（m＝1, 2, …, M）反演该位置处的脱硫塔内壁热流q；内壁热流沿高度方向变化，但是周向分布均匀，以第一步反演得到的内壁热流q作为壁厚反演中正问题的已知热边界条件，以高温区域的温度测量信息作为壁厚反演的依据，提高了测量信息对壁厚的灵敏度，有利于削弱壁厚检测问题的病态程度。
2.2 共轭梯度算法
利用共轭梯度算法求解壁厚反问题，通过迭代优化使得目标函数J(d)足够小或者达到最大迭代步imax，对应的壁厚d即为所求。目标函数J(d)可表示为：
$$J({d_i}) = \sum\limits_{k = 1}^K {{{[T_k^{{\rm{cal}}}({d_i}) - T_k^{{\rm{mea}}}]}^2}} \le \varepsilon $$ (5) 式中：Tkmea为在脱硫塔红外热像图上提取的第K个温度测量值；di为第i次迭代得到的壁厚的猜测值；Tkcal(di)是根据di进行正问题计算得到的第k个测量位置处的温度计算值。K为在红外热像图上提取的温度测量值的数目。停机标准ε可由下式表示：
$$ \varepsilon = K{\sigma ^2} $$ (6) 式中：σ为测量误差的标准差。
CGM沿着已知点处的梯度所构造出的共轭方向迭代搜索目标函数的极小点，迭代过程中对壁厚猜测值的修正可表示为：
$$ {d_i}_{ + 1} = {d_i} - {\alpha _i}{\mathit{\boldsymbol{\gamma }}_i} $$ (7) 式中：αi为搜索步长；γi为搜索方向。
搜索步长αi表示为：
$${\alpha _i}{\rm{ = }}\sum\limits_{k = 1}^K {[T_k^{{\rm{cal}}}({d_i}) - T_k^{{\rm{mea}}}]\nabla T_k^{{\rm{cal}}}({d_i}){\mathit{\boldsymbol{\gamma }}_i}} /\sum\limits_{k = 1}^K {\nabla T_k^{{\rm{cal}}}({d_i}){\mathit{\boldsymbol{\gamma }}_i}} $$ (8) 搜索方向γi可由下式表示：
$${\boldsymbol{\gamma} _i}{\rm{ = }}\nabla J({d_i}) + {\beta _i}{d_{i - 1}}$$ (9) 式中：▽J(di)为目标函数的梯度；βi为共轭系数，可根据式(10)计算：
$${\beta _i}{\rm{ = }}{\left[ {\nabla J({d_i})/\nabla J({d_{i - 1}})} \right]^2}$$ (10) 利用CGM根据红外热像图中正常区域温度反演该位置处的脱硫塔内壁热流q，其过程可参考公式(6)~(10)，在此就不一一赘述。
2.3 迭代求解流程
应用CGM根据脱硫塔红外热像图反演壁厚的计算步骤如下：
1）根据红外热像图，对异常区域进行辨识；
2）反演异常区域脱硫塔内壁热流q；
3）给出壁厚初始猜测值d0；
4）通过求解公式(1)~(4)，得到测点处的计算温度Tkmea（k＝1, 2, …, K），并代入公式(5)：
$$J({d_i}) = \sum\limits_{k = 1}^K {{{[T_k^{\rm{cal}}({d_i}) - T_k^{\rm{mea}}]}^2}} \leqslant \varepsilon $$ (11) 如果满足上述条件，di即为所求，停止迭代；否则继续；
5）按公式(8)~(10)对CGM里的参数进行更新；
6）根据公式(7)更新壁厚d的猜测值，并返回步骤4）。
3. 脱硫塔检测结果及分析
选取环境温度Tf＝20℃，对流换热系数h＝10 W/(m2·K)。异常区域温度测点数量K＝3；正常区域温度M＝2。考虑到实际测量过程中，温度测量误差是无法消除的，通过现场标定，测量误差σ＝0.055℃，ε＝0.01。
1）数值实验验证
为了验证检测系统的有效性和精确性，本文先进行脱硫塔内部缺陷检测的数值实验。在数值实验中，脱硫塔的热边界条件、几何参数和热物性参数均与实际过程相同，假设真实壁厚de＝16 mm。
数值实验中设置不同大小的测量误差，以考察测量误差对缺陷检测结果的影响。实验结果如表 2所示。
表 2 不同测量误差时的检测结果Table 2. The detection results of the different measurementerrorsMeasurement
error σDetect wall
thickness d/mmRelative
error/ %0.055 15.92 0.50 0.1 16.35 2.19 0.2 17.14 7.13 从表 2可以看出，随着测量误差的增大，壁厚检测结果的精确性下降。如σ＝0.2℃时，相对误差为7.13%，在工程上可以接受。
2）依据现场红外热像反演
① 高温区域1
如图 3所示，高温区域1的最高温度为48.2℃，区域的最大温差为1.4℃。利用基于导热反问题的脱硫塔壁厚检测方法，对高温区域1的壁厚进行计算，结果为d＝14.6 mm，即该区域玻璃鳞片厚度为0.6 mm。考虑到计算误差，可判定为防腐涂层已磨损殆尽或脱落，若不处理，塔壁金属将受到快速腐蚀。在检测后30天左右，脱硫塔停机检修，发现高温区域1的防腐涂层已脱落，证实了本方法的正确性。
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图 3 高温区域1对应的红外热像图Figure 3. Infrared image of the high temperature region 1(a) Infrared image of the tower (b) Local enlargement picture② 高温区域2
如图 4所示，高温区域2的最高温度为48.3℃，区域的最大温差为1.2℃。对高温区域2的壁厚进行反演：d＝15.8 mm，即该区域防腐涂层厚度为1.8 mm，可判定为防腐涂层已减薄。
③ 高温区域3
如图 5所示，高温区域3的最高温度为48.1℃，区域的最大温差为3.7℃。对高温区域3的壁厚进行反演：d＝6.5mm。表明该区域脱硫塔金属塔壁已发生腐蚀，减薄了7.5mm，应尽快排查、检修。
4. 结论
本文采用导热反问题的方法，根据红外热像图对运行中的脱硫塔壁厚进行了检测。其中，脱硫塔壁厚和脱硫塔内壁面热流的反演均采用共轭梯度法。首先通过数值实验，验证了本方法的可行性。然后，依据红外热像进行反演，发现脱硫塔筒体腐蚀1处，防腐涂层脱落1处，防腐涂层减薄1处。在后续的停机检修时对上述部进行了复核，均验证了上述检测结果，表明了基于表面红外热像的脱硫塔壁厚定量检测方法的有效性和准确性。
防腐涂层厚度的不一致，脱硫塔内介质分布的不均匀，红外热像仪精度以及环境等因素，可能会给壁厚检测结果引入误差，造成识别精度下降。如何提高壁厚检测精度，仍是下一步研究的方向。
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图 1 图像传感器工作原理和结构示意图：(a)，(b)，(c)和(d)分别为CCD、CMOS、前照式图像传感器结构和背照式图像传感器结构[12]；(e) 堆栈式CMOS图像传感器；(f) 具有Cu-Cu杂化键合的新型堆栈式背照CMOS图像传感器及器件截面图[13]
Figure 1. Schematic diagrams of imaging sensor working principles and structures: (a), (b), (c) and (d) are CCD, CMOS, structure of front-illuminated image sensor cross-section, and structure of back-illuminated image sensor cross-section, respectively[12]; (e) Stacked CMOS image sensor; (f) New stacked BI-CIS with Cu-Cu hybrid bonding and cross-sectional view of the device[13]
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图 2 有机、无机稀土掺杂化合物增强紫外图像传感器：(a) Lumogen结构；(b) Lumogen薄膜紫外-可见吸收光谱[37]；(c) 镀膜前(i)和镀膜后(ii)的CCD汞灯谱线[40]；(d) 不同方法制备的晕苯薄膜的反射和透射光谱图[42]；(e) 不同膜层厚度下的CMOS图像传感器在紫外波段范围内的量子效率[44]；(f) LiSr(1-3x/2)VO4: xTb3+的荧光激发和发射光谱[45]
Figure 2. Ultraviolet image sensor enhanced by organic and inorganic rare earth doped compounds: (a) Structure of Lumogen; (b) UV-vis absorption spectrum of Lumogen film[37]; (c) CCD mercury lamp spectra before (i)and after(ii) coating[40]; (d) Reflectance and transmittance spectra of Coronene film prepared by different methods[42]; (e)Quantum efficiency of CMOS image sensors in the ultraviolet band rang with different film thickness[44]; (f) Photoluminescence excitation and emission spectra of LiSr(1-3x/2)VO4: xTb3+[45]
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图 3 量子点增强紫外CMOS器件：(a) 纳米复合薄膜在紫外光和可见光照射下的示意图[57]；(b) CdSe/ZnS量子点和硅基量子点纳米复合物的吸收和荧光光谱图[58]；(c) 在可见光(i)和紫外光(ii)照射下的量子点涂层CID86器件[59]；(d) CdSe/ZnS量子点示意图；(e) 不同膜层的CdSe/ZnS量子点薄膜的荧光发射光谱[61]；(f) 量子点涂覆器件的结构图[60]
Figure 3. Quantum dot enhanced UV CMOS devices: (a) A schematic representation of the nanocomposites film illuminated by UV and visible light[57]; (b) Absorption and PL spectra of CdSe/ZnS QDs and QD/silica nanocomposites[58]; (c) Photoes of CID86 devicecoated by QD under visible (i) and UV (ii) light illumination[59]; (d) Diagram of CdSe/ZnS quantum dot; (e) PL emission spectra of CdSe/ZnS QD films with different layers[61]; (f) Schematic of a QD coated device[60]
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图 4 钙钛矿量子点增强紫外CCD器件：(a) 钙钛矿结构示意图；(b) CsPbX3胶体量子点溶液的荧光成像图和相应的荧光光谱[62]；(c) MAPbBr3量子点的紫外-可见吸收光谱和透射电镜图像[66]；(d) PQDCF紫外增强硅光电二极管结构示意图；(e) PQDCF旋涂前后的EMCCD成像传感器的外量子效率；(f) PQDCF的荧光光谱及在室内日光（上）和365 nm紫外灯下（下）的照片[68]
Figure 4. Perovskite quantum dots enhanced ultraviolet CCD devices: (a) Structure diagram of perovskite; (b) Photoes of CsPbX3 colloidal QDs solutions and corresponding PL spectra[61]; (c) UV-Vis absorption spectra and TEM image of MAPbBr3 QDs[65]; (d) Structure diagram of the PQDCF UV enhanced EMCCD; (e) The EQE of EMCCD image sensor before and after coating PQDCF, (f) PL spectrum of PQDCF with the corresponding photographs under ambient daylight (up) and under a 365 nm UV lamp (down) shown in inset[67]
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图 5 图像传感器在紫外成像方面的应用：(a) 盐酸二甲双胍可见透射和紫外吸收图像[4]；(b) 片剂的可见光和紫外图像[69]；(c)电站烟囱校准后的SO2图像[70]；(d) 高分辨率极紫外相机模型[72]；(e) 哈勃望远镜第三代相机的CCD探测器封装图[71]；(f) SUIT所有子系统的有效载荷[73]
Figure 5. Applications of image sensor in ultraviolet imaging: (a) UV and visible absorbance maps obtained for Glucophage SR[4]; (b) Visible and ultraviolet images of tablets[69]; (c) The resulting calibrated SO2 image of Drax power station stack[70]; (d) HRIEUV camera flight model[72]; (e) Peckaging image of CCD detector of Hubble telescope third generation camera[71]; (f) SUIT (Solar Ultraviolet Imaging Telescope) payload with all the subsystems[73]
表 1 CMOS与CCD图像传感器参数对比
Table 1 Comparison of CMOS and CCD image sensor parameters
Parameter CMOS CCD Signal to noise ratio Low High Sensitivity High Higher Size Small Large Power consumption High to mode rate High System complexity Low High Cost Low High Signal from pixel Voltage Electron packet Signal from chip Bits（digital） Analog voltage 
下载: 导出CSV
表 2 紫外增强CMOS/CCD图像传感器
Table 2 UV-enhanced CMOS/CCD image sensor
Year Sensor QE Wavelength range Number of pixels Ref. 1987 CCD 22%@250 nm 10-300 nm - [14] 1997 CCD 50% 200-400 nm - [15] 2007 CMOS 15%@300 nm 300 nm 4k×3k [16-17] 2008 CCD 45%@400 nm 250-900 nm 1k×1k [18] 2009 CMOS 52%@400 nm 400-1000 nm - [19] 2012 CCD 50% 180-200 nm 1024×512 [20] 2012 CMOS 50% 5-20 nm 1k×1k [21] 2013 CMOS - 200-1000 nm - [22] 2014 CMOS - - 3k×3k [23] 2015 CMOS 190-1000 nm 1k×1k [24] 2016 EMCCD 80%@205 nm 170-320 nm 1k×2k [25] 2016 CMOS - 200-1100 nm - [26] 2019 CMOS 46%@300 nm 190-1000 nm - [27] 2019 CMOS - 200-1000 nm 640×480 [28]
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