Electronic products combine tightly connected electrical, thermal, and mechanical behavior. Evaluating these effects separately can leave failures undiscovered until hardware is built, when redesign becomes expensive. Multiphysics simulation addresses this problem by examining interactions earlier, helping engineers identify weaknesses before committing to physical prototypes, tells Design News.
Guided workflows also make simulation more accessible to engineers without specialist analysis skills. Structural checks can assess drop, shock, and vibration performance against standards such as MIL-STD, IEC, and JEDEC. Integrating these checks into development helps teams investigate reliability while design changes remain practical, potentially reducing repeated prototype cycles and late corrections.
Writing in Design News, Keysight’s Chris Mueth explains why this approach matters across electric vehicles, data centers, aerospace systems, and advanced semiconductor packages. Heat, airflow, electromagnetic effects, and structural behavior increasingly influence one another, requiring coordinated engineering decisions.
Instead of handing work sequentially between departments, teams can analyze shared geometry in parallel. Mechanical, thermal, and radio-frequency specialists can assess competing requirements and resolve trade-offs before manufacturing begins. Physical testing then serves primarily to confirm predictions rather than reveal design problems.
Mueth illustrates the approach through a precision measurement instrument. At the system level, engineers evaluate enclosure manufacturing, cooling, transport loads, and structural durability. At the subsystem level, electromagnetic, circuit, and thermal simulations examine microwave performance and packaging effects under realistic conditions.
Board-level analysis checks power delivery, signal quality, and timing. Mechanical simulations also investigate solder joints, vias, and materials exposed to thermal cycling and vibration, helping reveal reliability problems that might otherwise emerge after deployment.
Connecting these levels enables continuous validation throughout development. The resulting structured engineering data can support AI workflows that coordinate and accelerate optimization. For engineering teams, the proposed benefit is earlier visibility into interactions, fewer costly redesigns, and more confidence that a product will perform reliably beyond ideal laboratory conditions.
