How AI Accelerates PMUT Design for Biomedical Ultrasonic Applications

Designing piezoelectric micromachined ultrasonic transducers for biomedical imaging and sensing applications requires balancing competing performance objectives like sensitivity and bandwidth while meeting strict frequency targets. Traditional sequential simulation-build-test cycles offer limited visibility into the global design space. This whitepaper demonstrates the Quanscient MultiphysicsAI workflow, which unites scalable cloud-based multiphysics simulation with accurate AI surrogate modeling to enable rapid inverse design. Through a case study optimizing four geometric parameters across 10,000 coupled piezoelectric-structural-acoustic simulations, the approach achieves validated performance improvements with minimal engineering overhead, transforming days of manual iteration into seconds of transparent, data-driven exploration on standard computational resources.