With the continuous shrinking of semiconductor manufacturing technology node, the pattern size has become smaller and the pattern density has become higher. This can cause the lithography defects to be more difficult to remove, because the developing fragments have a larger relative surface area, and thus more sticky to the patterned substrate surface. Although the defect can be reduced or removed by the rinse process, it may require more time and effort. As we know, the process optimization is related to the co-optimization of the rinse dispense volume, the nitrogen gas dispense recipe, and the wafer rotation speed. In the limit that we push the three parameters to the highest level, the defect can be removed. However, the adverse effect is that the resist patterns may be damaged, such as pattern collapse. It may also cause an increase in the cost for the process and more trouble shooting time in finding an optimizing recipe. It will be very helpful that we can develop a simulation program that can do the optimization. We present a model based on viscous fluid dynamics and calculate the removing force distribution across the 300-mm-diameter wafer for the defect residual. We assume that the defect, mostly the partially deprotected and developed photoresist polymer residual. We assume that once the removing force made by a sum of the flowing rinse water, the nitrogen gas, and the centrifugal force by wafer rotation reached certain threshold level, the defect can be removed. We have performed some simulation study and compared our results with previous studies. We found that we can reproduce the defect distribution patterns, such as the two rings, from the previous studies. From the simulation, we have learned the interrelations between the three parameters and find that we can get the minimally required strength from the three parameters for defect removal. We have also studied the situation of the 450-mm diameter wafer, and we found that we can get the defect clean result with reduced wafer rotation speed. In summary, we have built a defect model for photolithography development that can model the defect removal mechanism during the rinse process.