Vempati Srinivasa Rao
Papers
3
Total Citations
47
H-Index
3
About
Vempati Srinivasa Rao is a leading researcher at the intersection of semiconductor metrology and artificial intelligence, whose work is redefining how we inspect and measure advanced microelectronic packages. His primary research areas include 3D X-ray imaging, deep learning for automated defect detection, and semi-supervised learning for industrial applications. Rao’s major contributions lie in developing state-of-the-art deep learning models that can automatically detect, segment, and measure buried package features—such as Through Silicon Vias (TSVs) and Hybrid Bonding Materials (HBMs)—directly from 3D X-ray images, a task previously reliant on manual or rule-based methods. His most cited works, including "Automated Attribute Measurements of Buried Package Features in 3D X-ray Images using Deep Learning" (2021) and "Automated Detection and Segmentation of HBMs in 3D X-ray Images using Semi-Supervised Deep Learning" (2022), each have garnered 18 citations, demonstrating their immediate impact. Notably, Rao is pioneering the use of semi-supervised learning to overcome the scarcity of labeled industrial data, enabling robust 3D defect detection and metrology with minimal human annotation. His work is critical for the future of high-density packaging in semiconductors, directly supporting the yield and reliability of next-generation electronics.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3