The development of future Si device technologies will rely extensively on modeling, requiring truly predictive tools. Here we focus on the front-end processes, during which ion-implantation and annealing create 3-D impurity profiles that determine crucial electrical device parameters. The final configuration is the result of a complex interaction of dopant atoms with Si self-interstitials and vacancies, which themselves interact with each other as well as with the implantation-induced damage and interfaces. Predictive modeling requires for all these processes a solid understanding of the physical phenomena as well as accurate quantitative information. Si self-interstitials and vacancies are not observable directly in an experiment, but only via their interactions with some other physical quantity of the sample. We review our work employing dopant atoms in δ-doping superlattices (δ-DSL) that yield directly the time averaged depth profiles of Si native point defects during a particular processing sequence. This approach is uniquely suited for giving insights into the interplay of point defects in Si, providing crosschecks for atomistic calculations as well as parameters for process simulators. We describe experiments to extract interstitial and vacancy parameters and discuss the influence of intrinsic and extrinsic interstitial traps, as well as of the annealing environment, on the native point defect population. The latter allows to place certain bounds on the interstitial vacancy recombination coefficient as well as the ratio of interstitial and vacancy equilibrium concentrations.