In that race, MultiphysicsAI has proven to be a revolutionary platform. It fundamentally changes how piezoelectric micromachined ultrasonic transducers (PMUTs) are designed by integrating cloud-based finite element method (FEM) based simulation with AI-driven neural surrogates. This paradigm-shifting methodology increases the development speed and quality of PMUT design for biomedical applications. It empowers engineers to do their work to quickly explore complex design trade-offs.
The platform’s use of widely available cloud infrastructure means highly-skilled engineers can collaborate and conduct complex simulations without specialized hardware. It’s this accessibility that has the potential to democratize PMUT design. Additionally, it allows researchers and developers to more effectively optimize their devices. MultiphysicsAI seamlessly integrates sophisticated multiscale and multiphysics computational approaches with highly intuitive GUI interfaces. This extraordinary application shortens the design process, transforming days of painstaking iteration into seconds of data-driven exploration.
Optimizing Performance with MultiphysicsAI
MultiphysicsAI has expertise in optimizing project KPIs that are most important for PMUT success. These indicators are transmit sensitivity, center frequency, fractional bandwidth, and electrical impedance. In a recent use-case the platform was able to optimize four geometric parameters over 10,000 coupled piezoelectric-structural-acoustic simulations. The results proved that MultiphysicsAI was capable of producing AI surrogates with a mean error of less than 1% – a truly remarkable outcome.
Through the application of Pareto front optimization, engineers brought about stunning improvement in performance metrics. The fractional bandwidth increased from 65% to a record-breaking 100% with sensitivity increasing by 2-3 dB. Curiously, the platform was able to hold a center frequency of 12 MHz ±0.2% with little variation. Developments like these can drastically improve the performance of PMUTs in a diverse array of biomedical applications from imaging to therapeutic interventions.
Reducing Engineering Overhead with Rapid Simulations
Perhaps the most important benefit of MultiphysicsAI is its capacity to accelerate simulations and analysis by an order of magnitude. Traditional PMUT design processes tend to have time-consuming planning iterations that can take days or even weeks to finish. In comparison, MultiphysicsAI reaches sub-millisecond inference for all KPIs, enabling engineers to often make critical decisions in real-time. This predictive capability further shortens the design cycle while saving significant engineering overhead, allowing those resources to focus on other more mission-critical work.
Equipped with this transparent, data-driven exploration, engineers can feel empowered to make thoughtful design decisions that embrace the sensitivity vs. bandwidth tradeoff in a meaningful way. By leveraging machine learning algorithms, MultiphysicsAI transforms complex simulations into actionable insights, enabling teams to pursue innovative solutions without the typical constraints of time and resources.