scale circuits and for given ion strikes. A system designer, however, is more likely to be interested in the total error rate for a large circuit containing many transistors, and operating in some particular environment of interest. Because this is a very difficult problem, requiring detailed environmental models, the probability that a given ion strike causes an SEU is usually treated using analytical methods such as the rectangular parallelpided (RPP) model. Typical methods of solution have been covered in a previous short course and review articles [19,22,227]. Two groups have recently reported on large-scale SEU simulation systems which are aimed at predicting system error rates using a more first-principles basis of the interaction of ions with devices. II-53 The simpler of these two models [296] targeted the terrestrial alpha-particle-induced soft- error rate (SER) in SRAMs. In this system, analytic models with fitting parameters based on 3D device simulations were used for the alpha-induced charge collection and noise current. An equivalent circuit was extracted and circuit simulations were used to determine error margin, or the probability of an upset for a given incident alpha-particle energy and angle. In parallel with this, the probability of a given alpha-particle being emitted by the metal wiring (interconnects or on-chip solder bumps) and impinging on the circuit was computed from topography simulations and emission rates of the materials. The SER is then just the running summation of the probability that a given alpha-particle will be emitted multiplied by the probability that such an alpha-particle would cause an error. Comparisons of simulated and experimentally-observed error rates were reasonably close at low supply voltages, but only within a factor of five at higher voltages, with the simulations underestimating the observed SER [296]. The authors later rewrote the model to include neutron-generated reaction products and found improved agreement with accelerated data obtained using a high-flu