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3 October 2024 Automated Identification and classification of 2D materials for enhanced rapid prototyping efficiency: integration of deep learning and image processing techniques
Peter J. Van Kirk, Isaac Yu, Nitin Varshney, Navid Asadi, Elham Heidari, Hamed Dalir, Chandraman Patil
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Proceedings Volume PC13114, Low-Dimensional Materials and Devices 2024; PC131140G (2024) https://doi.org/10.1117/12.3028199
Event: Nanoscience + Engineering, 2024, San Diego, California, United States
Abstract
This study proposes a novel approach to streamline the manual identification and classification of 2D materials on-chip devices, crucial for rapid prototyping. Leveraging high-resolution imaging and smart stitching techniques, our method achieves a comprehensive representation of the material landscape. Advanced image processing algorithms, including mask-RCNN segmentation, extract key material attributes such as surface area and morphology. A tailored U-Net model is trained for precise material identification, encompassing parameters like composition and thickness. Performance evaluation involves state-of-the-art model architectures and hyperparameter optimization. By automating the material identification process and integrating with a sophisticated transfer system, manual intervention is minimized, expediting prototyping workflows. This framework not only enhances efficiency but also aligns with contemporary trends in materials science and machine learning research, fostering advancements in rapid prototyping capabilities.
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Peter J. Van Kirk, Isaac Yu, Nitin Varshney, Navid Asadi, Elham Heidari, Hamed Dalir, and Chandraman Patil "Automated Identification and classification of 2D materials for enhanced rapid prototyping efficiency: integration of deep learning and image processing techniques", Proc. SPIE PC13114, Low-Dimensional Materials and Devices 2024, PC131140G (3 October 2024); https://doi.org/10.1117/12.3028199
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KEYWORDS
2D materials
Image processing
Rapid manufacturing
Deep learning
Image classification
Image enhancement
Materials science
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