The reduction in transistor size in modern Integrated Circuits (ICs) has led to an increase in manufacturing defects within standard cells, also known as intra-cell defects. Detecting these defects and localizing them through diagnosis is crucial for achieving a fast yield ramp-up and ensuring a low test escape rate. Traditional fault models, such as stuckat and transition, do not effectively represent intra-cell defects. To address this issue, the Cell-Aware (CA) methodology was introduced. However, this technique involves time-consuming analog SPICE simulations to characterize standard cells. This paper presents a methodology, called Transistor Undetectable Defect eLiminator (TrUnDeL), based on graph theory to speed up the CA model generation process. Our methodology identifies undetectable defects for each stimulus applied at the inputs of the cell, which are then excluded from the analog simulations. We applied TrUnDeL to two libraries from STMicroelectronics (P28 and C28) to identify the undetectable stimulus/defect pairs, so that analog simulations are performed only on the remaining pairs. As a result, the CA model generation process time is reduced by a factor 3. Finally, we applied TrUnDeL to a SRAM bitcell case study and demonstrated that the obtained results are consistent with its existing CA model.