Most pattern shift analysis discussions focus on the accuracy of the Optical Proximity Correction (OPC) model that forms the pattern contours, while the OPC model's source itself is considered as a constant input to the model. In reality, the source might have defects or contaminations that can impact the image formation and possibly introduce asymmetrical pattern formation behavior. Initial studies have quantified the impact of source defects on wafer CDs in the presence of OPC . These studies have found that when source defects are present in the OPC model CD variation, NILS impact, MEEF impact, and pattern shifts might occur. Empirical studies and data have shown that the severity of defects are proportional to the impact on final pattern formation. However, it should also be noted that optical proximity correction schemes have been found to be a robust ally in countering the aforementioned defects in imaging. This study is a continuation of the previous work of source imperfection impacts on optical proximity correction to better understand the interaction between source defects and pattern shift during mask synthesis. Two variations of the study are executed: the first variation is the mask error case where random intensity variations are introduced in the pixelated source and an OPC model is created, then the corrected pattern is imaged with an ideal source. The second variation is the exposure error case where the OPC correction is performed with an ideal source, then exposed with a random defect in the manufacturing source. For both cases a pixel transmission variation is introduced in pixelated source using 11 various pixel selection methodology. Each experiment for the mask and exposure defects are conducted five times. This aims to quantify the effects on pattern uniformity while assuming defects in source manufacturing. This also allows you to better understand the limitation of scanner systems that might not be able to 100% represent the source pixels that were created during an aggressive Source Mask Optimization (SMO) session. Detailed analysis and studies are conducted to quantify the source defects impact on pattern formation.