As the pattern feature sizes become smaller, photomask assurance by one-dimensional criteria using a CD-SEM is reaching its limits. For instance, minute steps generated by OPC (Optical Proximity Correction), especially under the influence of corner rounding, are hard to measure. Thus, photomask assurance by means of two-dimensional features has been studied. Conventionally, in simulations to predict the printed shape on the wafer, OPCed data pattern have been used. While the OPCed data pattern represents the ideal pattern fidelity, actual pattern on a real photomask is different from the ideal shape. In addition, the increase of MEEF (Mask Error Enhancement Factor), along with the fine-than-ever pattern feature size, emphasizes the difference between the simulation result and the actually printed result on the wafer. To realize the two-dimensional assurance, we have to think of a method to predict the wafer image accurately. This is also important when we have to verify and manage the lithographic hotspots. For this purpose, we have been studying a mask model, a technique to take into consideration the actual pattern fidelity on the photomask, by modeling mask patterns' linearity, proximity, corner-rounding, etc., for each mask making process. By applying the mask model to OPCed design pattern, mask pattern shapes were found to be accurately predicted before mask making. Furthermore, we studied hotspot verification flow using the mask model. By the application of the mask model on the data pattern for the optical simulation, we accurately predicted the shape printed on the wafer, and accurately verify hotspots. This is expected to lead to assurance of photomask using two-dimensional shape.