Chavez-Angel Emigdio, Ng Ryan C, Sandell Susanne, He Jianying, Castro-Alvarez Alejandro, Torres Clivia M Sotomayor, Kreuzer Martin
Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, The Universitat Autònoma de Barcelona Campus, 08193 Barcelona, Spain.
NTNU Nanomechanical Lab, Department of Structural Engineering, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway.
Polymers (Basel). 2023 Jan 19;15(3):536. doi: 10.3390/polym15030536.
The thermal imaging of surfaces with microscale spatial resolution over micro-sized areas remains a challenging and time-consuming task. Surface thermal imaging is a very important characterization tool in mechanical engineering, microelectronics, chemical process engineering, optics, microfluidics, and biochemistry processing, among others. Within the realm of electronic circuits, this technique has significant potential for investigating hot spots, power densities, and monitoring heat distributions in complementary metal-oxide-semiconductor (CMOS) platforms. We present a new technique for remote non-invasive, contactless thermal field mapping using synchrotron radiation-based Fourier-transform infrared microspectroscopy. We demonstrate a spatial resolution better than 10 um over areas on the order of 12,000 um measured in a polymeric thin film on top of CaF substrates. Thermal images were obtained from infrared spectra of poly(methyl methacrylate) thin films heated with a wire. The temperature dependence of the collected infrared spectra was analyzed via linear regression and machine learning algorithms, namely random forest and k-nearest neighbor algorithms. This approach speeds up signal analysis and allows for the generation of hyperspectral temperature maps. The results here highlight the potential of infrared absorbance to serve as a remote method for the quantitative determination of heat distribution, thermal properties, and the existence of hot spots, with implications in CMOS technologies and other electronic devices.
在微尺度区域上以微米级空间分辨率对表面进行热成像仍然是一项具有挑战性且耗时的任务。表面热成像在机械工程、微电子学、化学过程工程、光学、微流体学和生物化学处理等领域是一种非常重要的表征工具。在电子电路领域,这项技术在研究互补金属氧化物半导体(CMOS)平台中的热点、功率密度和监测热分布方面具有巨大潜力。我们提出了一种基于同步辐射的傅里叶变换红外显微光谱技术,用于远程非侵入式、非接触式热场映射。我们展示了在CaF衬底上的聚合物薄膜中测量的约12000微米区域上,空间分辨率优于10微米。通过用导线加热聚甲基丙烯酸甲酯薄膜获得红外光谱,并从中得到热图像。通过线性回归以及机器学习算法(即随机森林算法和k近邻算法)分析收集到的红外光谱的温度依赖性。这种方法加快了信号分析速度,并允许生成高光谱温度图。这里的结果突出了红外吸收作为一种远程方法用于定量确定热分布、热性能和热点存在的潜力,这对CMOS技术和其他电子设备具有重要意义。