Bayesian-Optimized Collapse-Mode CMUT with Trenched Membrane for High Output Pressure

Capacitive micromachined ultrasonic transducers (CMUTs) have been extensively investigated for applications in medical imaging and industrial non-destructive testing. However, their relatively low acoustic pressure output remains a major limitation to broader adoption. This paper proposes a CMUT structure that combines collapse-mode operation with a trenched membrane to enhance output performance. An analytical model based on von Kármán large-deflection plate theory is developed to estimate the optimal radial position of the trench, thereby defining the search space for subsequent Bayesian optimization. Single-parameter sequential Bayesian optimizations are first performed to identify the individual effects and optimal ranges of the trench’s radial position, depth, and width. Subsequently, a three-parameter global Bayesian optimization framework is employed for global parameter refinement. The three-parameter joint optimization reveals strong synergistic interactions among the design variables, achieving a higher output pressure of 72.34 kPa compared to 70.02 kPa from sequential approaches. Under identical operating conditions, the optimized trenched membrane CMUT exhibits a 100.15% increase in output acoustic pressure and a 28.77% improvement in the pressure-bandwidth product compared to a uniform membrane. Statistical analysis across multiple independent runs yielded a coefficient of variation (CV) of only 0.052% for the output pressure in the three-parameter global optimization results. This confirms that the proposed framework robustly optimizes CMUT designs for high output pressure, offering a promising technical approach to enhancing device performance.