It is believed that smaller correction segments could achieve better pattern fidelity, however, some unstable OPC results
which are beyond the capability of common OPC correction schemes were found once the segment length is less than a
certain threshold. The dilemma between offering more degree-of-freedom by decreasing the correction segment length at
the cost of longer correction time and the instability induced by the reduced segment length challenges every OPC
In this paper, 2 indices are introduced; the segmentation index is proposed to determine a reasonable minimum segment
length while the stability index can be used to examine whether the correction system is a stiff convergence problem. A
compromised correction algorithm is also proposed to consider the OPC accuracy, stability and runtime simultaneously.
The correction results and the runtime are analyzed.
Optical proximity correction (OPC) is usually used to pre-distort mask layouts to make the printed patterns as close to the desired shapes as possible. For model-based OPC, a lithographic model to predict critical dimensions after lithographic processing is needed. The model is usually obtained via a regression of parameters based on experimental data containing optical proximity effects. When the parameters involve a mix of the continuous (optical and resist models) and the discrete (kernel numbers) sets, the traditional numerical optimization method may have difficulty handling model fitting. In this study, an artificial-intelligent optimization method was used to regress the parameters of the lithographic models for OPC. The implemented phenomenological models were constant-threshold models that combine diffused aerial image models with loading effects. Optical kernels decomposed from Hopkin’s equation were used to calculate aerial images on the wafer. Similarly, the numbers of optical kernels were treated as regression parameters. This way, good regression results were obtained with different sets of optical proximity effect data.