Last year, NVIDIA introduced its cuLitho software library, which promises to speed up photomask development by up to 40 times. Today, NVIDIA announced a partnership with TSMC and Synopsys to implement its computational lithography platform for production use, and use the company’s next-generation Blackwell GPUs for AI and HPC applications.
The development of photomasks is a crucial step for every chip ever made, and NVIDIA’s cuLitho platform, enhanced with new generative AI algorithms, significantly speeds up this process. NVIDIA says computational lithography consumes tens of billions of hours per year on CPUs. By leveraging GPU-accelerated computational lithography, cuLitho substantially improves over traditional CPU-based methods. For example, 350 NVIDIA H100 systems can now replace 40,000 CPU systems, resulting in faster production times, lower costs, and reduced space and power requirements.
NVIDIA claims its new generative AI algorithms provide an additional 2x speedup on the already accelerated processes enabled through cuLitho. This enhancement is particularly beneficial for the optical proximity correction (OPC) process, allowing the creation of near-perfect inverse masks to account for light diffraction.
TSMC says that integrating cuLitho into its workflow has resulted in a 45x speedup of curvilinear flows and an almost 60x improvement in Manhattan-style flows. Curvilinear flows involve mask shapes represented by curves, while Manhattan mask shapes are restricted to horizontal or vertical orientations.
Synopsys, a leading developer of electronic design automation (EDA), says that its Proteus mask synthesis software running on the NVIDIA cuLitho software library has accelerated computational workloads compared to current CPU-based methods. This acceleration is crucial for enabling angstrom-level scaling and reducing turnaround time in chip manufacturing.
The collaboration between NVIDIA, TSMC, and Synopsys represents a significant advancement in semiconductor manufacturing in general and cuLitho adoption in particular. By leveraging accelerated computing and generative AI, the partners are pushing semiconductor scaling possibilities and opening new innovation opportunities in chip designs.
