Using well-calibrated TCAD simulations that reproduce measurements, we investigate the joint impact of device variability and transistor aging on the data integrity of SRAMs implemented using 22 FDSOI. The calibrations are done against measurement data for both I-V characteristics and variability data. All failure analyses were accurately performed in TCAD mix-mode simulations for a complete 6-T SRAM cell. Finally, towards investigating further how such errors impact the system level, we explore the corresponding induced accuracy drop in Deep Neural Networks (DNNs). Different quantized NNs are studied and their sensitivity to errors in weights and activations is also explored.