Thermal Property Microscopy with Compressive Sensing Frequency-Domain Thermoreflectance
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913945470631936 |
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| author | Yang, Haobo Zhu, Zhenguo Xie, Zhongnan Du, Jinhong Bai, Shuo Guo, Hong Liu, Te-Huan Yang, Ronggui Qian, Xin |
| author_facet | Yang, Haobo Zhu, Zhenguo Xie, Zhongnan Du, Jinhong Bai, Shuo Guo, Hong Liu, Te-Huan Yang, Ronggui Qian, Xin |
| contents | Spatial mapping of thermal properties is critical for unveiling the structure-property relation of materials, heterogeneous interfaces, and devices. These property images can also serve as datasets for training artificial intelligence models for material discoveries and optimization. Here we introduce a high-throughput thermal property imaging method called compressive sensing frequency domain thermoreflectance (CS-FDTR), which can robustly profile thermal property distributions with micrometer resolutions while requiring only a random subset of pixels being experimentally measured. The high-resolution thermal property image is reconstructed from the raw down-sampled data through L_1-regularized minimization. The high-throughput imaging capability of CS-FDTR is validated using the following cases: (a) the thermal conductance of a patterned heterogeneous interface, (b) thermal conductivity variations of an annealed pyrolytic graphite sample, and (c) the sharp change in thermal conductivity across a vertical aluminum/graphite interface. With less than half of the pixels being experimentally sampled, the thermal property images measured using CS-FDTR show nice agreements with the ground truth (point-by-point scanning), with a relative deviation below 15%. This work opens the possibility of high-throughput thermal property imaging without sacrificing the data quality, which is critical for materials discovery and screening. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04728 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Thermal Property Microscopy with Compressive Sensing Frequency-Domain Thermoreflectance Yang, Haobo Zhu, Zhenguo Xie, Zhongnan Du, Jinhong Bai, Shuo Guo, Hong Liu, Te-Huan Yang, Ronggui Qian, Xin Applied Physics Materials Science Spatial mapping of thermal properties is critical for unveiling the structure-property relation of materials, heterogeneous interfaces, and devices. These property images can also serve as datasets for training artificial intelligence models for material discoveries and optimization. Here we introduce a high-throughput thermal property imaging method called compressive sensing frequency domain thermoreflectance (CS-FDTR), which can robustly profile thermal property distributions with micrometer resolutions while requiring only a random subset of pixels being experimentally measured. The high-resolution thermal property image is reconstructed from the raw down-sampled data through L_1-regularized minimization. The high-throughput imaging capability of CS-FDTR is validated using the following cases: (a) the thermal conductance of a patterned heterogeneous interface, (b) thermal conductivity variations of an annealed pyrolytic graphite sample, and (c) the sharp change in thermal conductivity across a vertical aluminum/graphite interface. With less than half of the pixels being experimentally sampled, the thermal property images measured using CS-FDTR show nice agreements with the ground truth (point-by-point scanning), with a relative deviation below 15%. This work opens the possibility of high-throughput thermal property imaging without sacrificing the data quality, which is critical for materials discovery and screening. |
| title | Thermal Property Microscopy with Compressive Sensing Frequency-Domain Thermoreflectance |
| topic | Applied Physics Materials Science |
| url | https://arxiv.org/abs/2506.04728 |