The computer cost for mask data processing grows increasingly more expensive every year.
However the Graphics Processing Unit (GPU) has evolved dramatically. The GPU which
originally was used exclusively for digital image processing has been used in many fields of
numerical analysis. We developed mask data processing techniques using GPUs together with
distributed processing that allows reduced computer costs as opposed to a distributed processing
system using just CPUs.
Generally, for best application performance, it is important to reduce conditional branch
instructions, to minimize data transfer between the CPU host and the GPU device, and to optimize
memory access patterns in the GPU. Hence, in our optical proximity correction (OPC), the light
intensity calculation step, that is the most time consuming part of this OPC, is optimized for GPU
implementation and the other inefficient steps for GPU are processed by CPUs . Moreover, by
fracturing input data and balancing a computational road for each CPU, we have put the powerful
distributed computing into practice.
Furthermore we have investigated not only the improvement of software performance but also how
to best balance computer cost and speed, and we have derived a combination of the CPU hosts and
the GPU devices to maximize the processing performance that takes computer cost into account .
We have also developed a recovery function that continues OPC processing even if a GPU breaks
down during mask data processing for a production. By using the GPUs and distributed
processing, we have developed a mask data processing system which reduces computer cost and has