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%
No comments:
Post a Comment