In-situ acoustic characterization of a MegPie megasonic transducer for photomask cleaning using a wireless sensor array

Cleaning processes in advanced 193i and EUV megasonic photomask cleaning continue to have demanding defect requirements, which rely on a narrow process window. As patterns continue to shrink, the process challenges remain increasingly complex. Many parameters must be optimized and controlled to ensure full particle removal and pattern damage control, including the transducer output pressure, drive frequency, power setting, transducer position, flow rate, gas concentration, temperature, chemistry. Furthermore, the dynamic nature of the cleaning process consists of a rotating photomask substrate and chemistry delivered at a specific flow rate and temperature. Despite early exploratory work from a wired photomask-shaped sensor array, where a stationary double nozzle and mask chuck were used, it became evident quickly that a wireless in-situ measurement was necessary to characterize how the dynamic conditions affect the acoustic cavitation behavior. This led to the development of a prototype of the wireless sensor to evaluate a single nozzle megasonic system that swept across the rotating photomask sensor. This allowed to study how the acoustic behavior changes with increasing linear velocity as the nozzle moves from center to edge. In this study, the technology was further enhanced by improving the form factor and data acquisition capabilities to test a single crystal 1 MHz transducer (i.e., ProSys^® MegPie^TM) fixed in position above the rotating substrate. The acoustic pressure uniformity across the photomask was evaluated for varying parameters, including the mask rotational speed, gap distance, generator power, and exposure time. Acoustic spectra exhibited distinct signatures that may be indicative of cleaning performance, specifically particle removal and pattern damage. The high costs of advanced photomask processes have demanded a zero-defect requirement, a constraint prevalent across the semiconductor industry. The study aims to develop an in-situ measurement solution that accounts for all the representative process variables that influence the acoustic behavior and ultimately the cleaning performance.