Increased sensitivity in near infrared hyperspectral imaging by enhanced background noise subtraction

Q3 Chemistry Journal of Spectral Imaging Pub Date : 2019-01-10 DOI:10.1255/JSI.2019.A2
T. Mehl, G. Wyller, I. Burud, E. Olsen
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引用次数: 1

Abstract

Near infrared hyperspectral photoluminescence imaging of crystalline silicon wafers can reveal new knowledge on the spatial distribution and the spectral response of radiative recombination active defects in the material. The hyperspectral camera applied for this imaging technique is subject to background shot noise as well as to oscillating background noise caused by temperature fluctuations in the camera chip. Standard background noise subtraction methods do not compensate for this oscillation. Many of the defects in silicon wafers lead to photoluminescence emissions with intensities that are one order of magnitude lower than the oscillation in the background noise level. These weak signals are therefore not detected. In this work, we demonstrate an enhanced background noise subtraction scheme that accounts for temporal oscillations as well as spatial differences in the background noise. The enhanced scheme drastically increases the sensitivity of the camera and hence allows for detection of weaker signals. Thus, it may be useful to implement the method in all hyperspectral imaging applications studying weak signals.
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通过增强背景噪声减法提高近红外高光谱成像的灵敏度
晶体硅片的近红外高光谱光致发光成像可以揭示材料中辐射复合活性缺陷的空间分布和光谱响应。应用于该成像技术的高光谱相机受到背景散粒噪声以及相机芯片中的温度波动引起的振荡背景噪声的影响。标准的背景噪声相减方法不能补偿这种振荡。硅片中的许多缺陷导致光致发光发射,其强度比背景噪声水平的振荡低一个数量级。因此,这些弱信号没有被检测到。在这项工作中,我们展示了一种增强的背景噪声减法方案,该方案考虑了背景噪声的时间振荡和空间差异。增强的方案戏剧性地增加了相机的灵敏度,因此允许检测较弱的信号。因此,将该方法应用于研究弱信号的所有高光谱成像应用中可能是有用的。
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来源期刊
Journal of Spectral Imaging
Journal of Spectral Imaging Chemistry-Analytical Chemistry
CiteScore
3.90
自引率
0.00%
发文量
11
审稿时长
22 weeks
期刊介绍: JSI—Journal of Spectral Imaging is the first journal to bring together current research from the diverse research areas of spectral, hyperspectral and chemical imaging as well as related areas such as remote sensing, chemometrics, data mining and data handling for spectral image data. We believe all those working in Spectral Imaging can benefit from the knowledge of others even in widely different fields. We welcome original research papers, letters, review articles, tutorial papers, short communications and technical notes.
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