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This whitepaper provides MEMS engineers, biomedical device developers, and multiphysics simulation specialists with a practical AI-accelerated workflow for optimizing piezoelectric micromachined ultrasonic transducers (PMUTs), enabling you to explore complex design trade-offs between sensitivity and bandwidth while achieving validated performance improvements in minutes instead of days using standard cloud infrastructure.
What you will learn about:
- MultiphysicsAI combines cloud-based FEM simulation with neural surrogates to transform PMUT design from trial-and-error iteration into systematic inverse optimization
- Training on 10,000 randomized geometries produces AI surrogates with 1% mean error and sub-millisecond inference for key performance indicators: transmit sensitivity, center frequency, fractional bandwidth, and electrical impedance
- Pareto front optimization simultaneously increases fractional bandwidth from 65% to 100% and improves sensitivity by 2-3 dB while maintaining 12 MHz center frequency within ±0.2%
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IEEE Spectrum and Wiley are proud to bring you this white paper, sponsored by Quanscient.
More Information
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.
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