Maximizing utilization of large-scale mask data preparation clusters

With CMOS technology nodes going further into the realm of sub-wavelength lithography, the need for compute power also increases to meet runtime requirements for reticle enhancement techniques and results validation. Expanding the mask data preparation (MDP) cluster size is an obvious solution to increase compute power, but this can lead to unforeseen events such as network bottlenecks, which must be taken into account. Advanced scalable solutions provided by optical proximity correction (OPC)/mask process correction (MPC) software are obviously critical, but other optimizations such as dynamic CPU allocations (DCA) based on real CPU needs, high-level jobs management, real-time resource monitoring, and bottleneck detection are also important factors for improving cluster utilization in order to meet runtime requirements and handle post-tapeout (PTO) workloads efficiently. In this paper, we will discuss tackling such efforts through various levels of the “cluster utilization stack” from low CPU levels to business levels to head towards maximizing cluster utilization and maintaining lean computing.