Accelerating Computational Lithography Using Massively Parallel GPU Rasterizer

Siemens EDA's whitepaper explores the use of massively parallel GPU rasterization to address the challenges in computational lithography, where accuracy and throughput are crucial. Traditional rasterization techniques struggle with the precision required for nanometer-scale lithography, making GPUs an attractive solution due to their parallel processing capabilities. The GPU-optimized rasterizer decomposes layouts into regions for parallel processing, ensuring nanometer-scale precision and connectivity preservation. Real-world performance results using Nvidia H100 GPUs show significant speedups without compromising accuracy, leading to improved yield and reduced time to market in semiconductor manufacturing. This shift towards GPU-accelerated rasterization offers scalability and future-proofing for advanced chip design workflows.