one-dimensional drift-diffusion (1D-DD) simulation, is much faster. However, such simulations neglect the actual 3D-randomness of the material, and only yield a spa tially uniform current density. Accordingly, it would be an advance in the art t o provide improved simulation of disordered semiconductor devices. This work provides improved charge transport modeling of disordered semicon ductor devices. The general modeling framework employed is 3D master equation modeling. More specifically, charge transport is modeled as incoherent hopping b e tween localized molecular states, and recombination is modeled as a nearest-neighbor process where an electron at a first location and a hole at a second location can r e combine at either the first location or the second location. Here the first and second locations are any pair of nearest neighbor locations. We have found that this nearest neighbor recombination model performs substantially better than the conventional local recombination model where an electron and a hole must be at the same loca tion t o recombine. The recombination rate is modeled as a product of a prefactor , hopping rates and state occupancies. Importantly, we have found that sufficient simulation accuracy can be obtained by taking t o be given by an empirically derived analytic expression. For the specific conditions and assumptions considered in detail below, the recom bination rate prefactor is given by It is expected that similar forms for the recombination rate prefactor can also be derived in other circumstances. The main advantage of a recombination rate prefactor expressed in such a simple form is that it is independent of local conditions in the device, such as carrier con centrations. This simplicity greatly reduces run time compared t o a situation where local variations in 7 would have t o be accounted for. As shown in detail below, this result for 7 is surprising and unexpected, because many contributions t o 7 have a significant dependence on carrier concentrations (i.e., the curves on Fig. 12, parts (a)-(d)). However, the concentration dependence of the net result of Fig. 12 part (e) SUMMARY