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About

Ramanpreet Singh Pahwa is a leading researcher at the intersection of deep learning, 3D X-ray imaging, and semiconductor metrology. His work focuses on developing automated, semi-supervised deep learning approaches for the detection, segmentation, and metrology of buried package features and high-bandwidth memory (HBM) structures in 3D X-ray scans. Pahwa’s major contributions include pioneering state-of-the-art models that enable robust, accurate, and efficient attribute measurements of complex 3D structures, significantly advancing defect detection and quality control in semiconductor manufacturing. His most cited papers, including “Automated Attribute Measurements of Buried Package Features in 3D X-ray Images using Deep Learning” and “Automated Detection and Segmentation of HBMs in 3D X-ray Images using Semi-Supervised Deep Learning,” have each garnered 18 citations, demonstrating their impact on both academic research and industrial applications. Pahwa has also explored object detection performance across varied data distributions and developed adaptable models for autonomous service robots. His work is notable for bridging cutting-edge 3D deep learning techniques with practical, high-stakes challenges in semiconductor inspection, making him a key figure in the advancement of automated metrology and defect analysis.