An innovative, AI-driven approach for non-intrusive analysis and defect detection in multi-tiered chiplets, enhancing microelectronics packaging.
This technology introduces an automated, AI-assisted vision method for the analysis of multi-tiered chiplets, focusing on identifying structural defects or voids without the need for invasive procedures. It leverages advanced machine learning models to convert temperature maps into detailed three-dimensional defect maps, offering a comprehensive insight into the structural integrity of microelectronics. This method is particularly crucial in the context of 3D stacking in microsystems, where seamless bonding is essential for device functionality.
· Quality control and assurance in semiconductor manufacturing.
· Advanced electronics packaging, particularly for 3D stacked microelectronics.
· Defect analysis and prevention in microsystem development.
· Research and development in microelectronics and materials science
· Speed: Offers rapid measurements compared to traditional methods, providing rich features.
· Simplicity: Eliminates the need for a medium, such as water, thus avoiding potential sample damage and reducing processing time.
· Efficiency: Enhances defect detection, improving yield rates and leading to cost savings in semiconductor manufacturing.
· Intelligence: Utilizes machine learning to predict defects, understanding the link between manufacturing processes and defect evolution.
Patent Pending