Sandia National Laboratories announced on Tuesday that it has developed computer models that can provide both qualitative and quantitative data on how the hundreds of solder joints in a nuclear weapon will perform over time.
Sandia Labs has been using computational modeling to predict the performance of solder joints for more than 30 years. Recent breakthroughs, however, now allow models to generate quantitative predictions based on the physical behavior of a material in a design.
Paul Vianco, a materials scientist at Sandia Lab’s Kansas City National Security Campus, said earlier models only gave designers a broad reliability window for various solder joints. That type of qualitative analysis did not provide enough data to guide the designs of electronic assemblies, however.
“Computational modeling of solder joint fatigue has become critical to Sandia and its role in the current nuclear weapons life extension programs, even before production assembly at the Kansas City National Security Campus,” Vianco said. “Sandia uses the computational model to solve manufacturing issues as well as assess the impact of design changes on solder joint reliability. This is critical as we finalize designs and head into production.”
Quantitative computer modeling can reduce the design time required from months to weeks, Vianco said. Eliminating the need to fabricate samples and to perform equipment operations and data analysis leads to significant time and cost savings.
Sandia Lab researchers validate and forecast solder fatigue cracks by comparing a model’s predictions to experimental results for components like surface mount resistors and capacitors, leadless ceramic chip carriers, and plastic ball grid arrays, just to name a few.
“Close collaboration between materials scientists and materials modelers is essential for the creation of good material models,” Mike Neilsen, a material modeler at Sandia Labs, said. “We can accurately predict crack initiation locations, crack paths and the number of cycles needed to grow fatigue cracks.”