MultiphysicsAI is changing the game with a data-driven design for PMUT. It’s doing that by merging cloud-based finite element method (FEM) simulation with neural surrogates. Perfecting this new approach turns the past trial and error practice into a systematic inverse optimization process. Therefore, it significantly improves the efficiency and efficacy of biomedical PMUT development.
Now they can play with the intricate design trade-offs between fundamental parameters such as sensitivity and bandwidth. Its performance is significantly improved by its unprecedented capabilities. You can test these improvements within minutes, as opposed to the days typically required for conventional design approaches. This transformation not only accelerates the design process but opens new avenues for research and application in various fields.
Optimizing Design Parameters
MultiphysicsAI has already been used to inform the optimal values of four geometric parameters over 10,000 coupled piezoelectric-structural-acoustic simulations. The system autonomously produces AI surrogates, which perform with an astounding average error rate of just 1%. This has kept performance predictions extremely conservative. The time it takes to run a model for key performance indicators has been largely minimized down to less than a millisecond. This allows engineers to arrive at better, faster-informed decisions during the design phase.
Additional key performance indicators are transmit sensitivity, center frequency, fractional bandwidth and electrical impedance. PMUT engineering engineers can use these metrics as a basis for optimizing PMUT designs to suit the unique needs of any given application. The MultiphysicsAI powered Pareto front optimization has increased the fractional bandwidth from 65% to an amazing 100%. Further, it has increased sensitivity by 2-3 decibels, all of which is achieved without changing the center frequency of 12 MHz by more than ±0.2%.
Transforming the Engineering Process
With MultiphysicsAI now in production, engineering overhead for PMUT design is being decreased by an order of magnitude. These conventional approaches are typically hundreds of thousands of iterations that require tedious manual review, which is both costly and labor-intensive. Previously, this would take months or even years—but now, thanks to transparent, data-driven exploration, MultiphysicsAI reduces this process to a matter of seconds. This significant reduction in design time frees engineers to innovate instead of becoming mired in repetitive tasks.
Moreover, MultiphysicsAI runs on off-the-shelf computational resources, further democratizing its use to a broad base of users. This democratization of technology allows more professionals to engage in advanced PMUT design without the need for specialized hardware or extensive technical expertise.
Future Implications
The cutting-edge developments introduced by the use of MultiphysicsAI do more than streamline the design process. It holds profound potential in biomedical applications. At the same time, the demand for new, more efficient and effective ultrasonic transducers is growing exponentially. These are the kinds of challenges that tools like MultiphysicsAI will be vital in addressing. Engineers and researchers are able to perform fast, highly accurate simulations. This unique capability gives them the ability to stretch the frontiers of medical diagnostics and treatment technologies.