A sparse matrix model-based optical proximity correction algorithm with model-based mapping between segments and control sites

Optical proximity correction (OPC) is a key step in modern integrated circuit (IC) manufacturing.The quality of model-based OPC (MB-OPC) is directly determined by segment offsets after OPC processing.However,in conventional MB-OPC,the intensity of a control site is adjusted only by the movement of its corresponding segment;this scheme is no longer accurate enough as the lithography process advances.On the other hand,matrix MB-OPC is too time-consuming to become practical.In this paper,we propose a new sparse matrix MB-OPC algorithm with model-based mapping between segments and control sites.We put forward the concept of 'sensitive area'.When the Jacobian matrix used in the matrix MB-OPC is evaluated,only the elements that correspond to the segments in the sensitive area of every control site need to be calculated,while the others can be set to 0.The new algorithm can effectively improve the sparsity of the Jacobian matrix,and hence reduce the computations.Both theoretical analysis and experiments show that the sparse matrix MB-OPC with model-based mapping is more accurate than conventional MB-OPC,and much faster than matrix MB-OPC while maintaining high accuracy.

Optical proximity correction （OPC） is a key step in modem integrated circuit （IC） manufacturing. The quality of model-based OPC （MB-OPC） is directly determined by segment offsets after OPC processing. However, in conventional MB-OPC, the intensity of a control site is adjusted only by the movement of its corresponding segment; this scheme is no longer accurate enough as the lithography process advances. On the other hand, matrix MB-OPC is too time-consuming to become practical. In this paper, we propose a new sparse matrix MB-OPC algorithm with model-based mapping between segments and control sites. We put forward the concept of＇sensitive area＇. When the Jacobian matrix used in the matrix MB-OPC is evaluated, only the elements that correspond to the segments in the sensitive area of every control site need to be calculated, while the others can be set to 0. The new algorithm can effectively improve the sparsity of the Jacobian matrix, and hence reduce the computations. Both theoretical analysis and experiments show that the sparse matrix MB-OPC with model-based mapping is more accurate than conventional MB-OPC, and much faster than matrix MB-OPC while maintaining high accuracy.