A significant leap in the design of piezoelectric micromachined ultrasonic transducers (PMUTs) is achieved by the incorporation of MultiphysicsAI. This cutting-edge platform unifies cloud-based FEM simulation with neural surrogate models. It makes the optimization process more efficient and productive. By replacing traditional trial-and-error methodologies with a systematic inverse optimization workflow, MultiphysicsAI is pioneering the future of biomedical ultrasonic applications.
MultiphysicsAI is transforming the way PMUTs are designed and optimized. It allows designers to accurately explore the often complicated trade-offs between sensitivity and bandwidth. This scanning potential is of great importance to engineers attempting to design smaller, more efficient, and more effective ultrasonic devices. The platform uses state-of-the-art AI surrogates. These surrogates are built for each geometry through extensive training on 10,000 randomized geometries, letting us validate performance improvements in minutes rather than days.
Optimizing Performance with AI Surrogates
The AI surrogates produced by MultiphysicsAI provide outstanding accuracy, achieving a mean error of just 1%. Sub-millisecond inference times take this precision to the next level. Transmit sensitivity, center frequency, fractional bandwidth, and electrical impedance are just some key performance indicators that benefit from these fast and accurate intuitions. By optimizing four geometric parameters across 10,000 coupled piezoelectric-structural-acoustic simulations, the platform enables users to efficiently assess and improve PMUT designs.
Perhaps the single best accomplishment of MultiphysicsAI was to increase the optimal fractional bandwidth, from 65% to a stunning 100%. This specific improvement increases sensitivity by an impressive 2-3 dB. Additionally, it maintains the center frequency at 12 MHz, with a tolerance of ±0.2%. These validated performance improvements pave the way for the platform to revolutionize the design process across the industry.
Transforming Design Iterations into Data-Driven Exploration
With MultiphysicsAI, extensive manual iterations are no longer required to design a proper PMUT. It takes that process and turns it into seconds of fairly transparent, data-driven exploration. This transition greatly speeds up the design process, and more importantly, greatly lowers the engineering overhead. By using widely available cloud infrastructure and expertise, engineers are able to gain proven boosts in performance without extensive investment.
The impacts of this new technology can go much further than just enabling quicker design cycles. By providing a systematic approach, guided by scientific principles, MultiphysicsAI enables users to make more informed decisions based on the intricate complexity associated with PMUT design. With AI, engineers are better equipped to adjust projects under real-time data and insights rather than using passive reactions built on assumptions.