7cdf2f99d2dd915c2523a49a76f07947 Poli_a2023v15n3p536iENG.pdf 061c0758a23c54a694b9868eabd5d69320f8309f Poli_a2023v15n3p536iENG.pdf 5c0489a7a828532bef013849310f74c60b5cebfe16018fe161e244b79941450e Poli_a2023v15n3p536iENG.pdf Title: Application of Synchrotron Radiation-Based Fourier-Transform Infrared Microspectroscopy for Thermal Imaging of Polymer Thin Films Subject: 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 um2 measured in a polymeric thin film on top of CaF2 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. Keywords: thermal imaging; synchrotron radiation; machine learning; temperature dependence; FTIR polymer; FTIR thermometry Author: Emigdio Chavez-Angel, Ryan C. Ng, Susanne Sandell, Jianying He, Alejandro Castro-Alvarez, Clivia M. Sotomayor Torres and Martin Kreuzer Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Tue Jan 31 09:01:13 2023 CET ModDate: Tue Jan 31 09:04:45 2023 CET Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 13 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 2618116 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- PTRULH+VnURWPalladioL Type 1 Custom yes yes no 10 0 QYLBFS+URWPalladioL-Bold Type 1 Custom yes yes no 15 0 NZLLXW+URWPalladioL-Roma Type 1 Custom yes yes no 20 0 XIWLYX+URWPalladioL-Ital Type 1 Custom yes yes no 24 0 TLFMRE+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 42 0 YIVWYZ+CMSY10 Type 1 Builtin yes yes no 67 0 OVNEXW+EURM10 Type 1 Builtin yes yes no 71 0 BCDEEE+PalatinoLinotype-Bold TrueType WinAnsi yes yes no 83 0 BCDFEE+PalatinoLinotype-Roman TrueType WinAnsi yes yes no 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PDFMetadata: Objects: 466 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@44c8afef Item: Title: Materials and Methods Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@7b69c6ba Children: Item: Title: Sample Preparation Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@46daef40 Item: Title: Metal Wire Preparation and Temperature Calibration Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@12f41634 Item: Title: Synchrotron Radiation-Based Fourier-Transform Infrared (SR-FTIR) Microspectroscopy Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@13c27452 Item: Title: Machine Learning Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@262b2c86 Item: Title: Results and Discussion Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@371a67ec Children: Item: Title: PMMA Infrared Absorption (Calibration Data) Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@5ed828d Item: Title: Line Scans (Test Sample) Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@50d0686 Children: Item: Title: Temperature Prediction by Linear Regression Model Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@7a3d45bd Item: Title: Temperature Prediction by Machine Learning Approach Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1e7c7811 Item: Title: Hyperspectral Imaging Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@77ec78b9 Item: Title: Summary Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1a3869f4 Item: Title: References Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@a38d7a3 Info: Title: Application of Synchrotron Radiation-Based Fourier-Transform Infrared Microspectroscopy for Thermal Imaging of Polymer Thin Films Author: Emigdio Chavez-Angel, Ryan C. Ng, Susanne Sandell, Jianying He, Alejandro Castro-Alvarez, Clivia M. Sotomayor Torres and Martin Kreuzer Subject: 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 um2 measured in a polymeric thin film on top of CaF2 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. 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