|
Acoustic cavitation continues to be widely used in advanced 193i and EUV megasonic photomask cleaning [1]. However, process challenges remain complex as patterns become smaller [2]. Operating within a narrow process window that ensures both full particle removal and pattern damage control is a balancing act that requires many parameters to be optimized and controlled. These include the transducer type, drive frequency, power setting, flow rate, gas concentration, temperature, chemistry, and transducer position [3, 4, 5, 6, 7]. While batch processing may be more economical for less critical cleaning steps, advanced lithography processes rely on single photomask cleaning technologies because of the increased need for within mask control. Improved cleaning uniformity is achieved through the continuous movement of the photomask and transducer. In-situ measurement of the acoustic field is required to correlate acoustic parameters with cleaning performance. Previous work introduced a photomask-shaped cavitation sensor array wired to a cavitation meter which characterized how acoustic cavitation varied with parameters such as drive frequency, generator power, transducer distance, and sensor position correlated with cavitation pressure under a static condition [7]. In this study, the technology was extended by developing a wireless sensor array to incorporate the dynamic effects of the photomask rotation and the transducer arm translation. The acoustic pressure uniformity across the photomask was evaluated for varying parameters, including mask rotational speed, transducer arm speed, and exposure time. Pressure measurements of the direct field (P0), stable cavitation (Ps), and transient cavitation (Pt) exhibited distinct signatures that may be indicative of cleaning performance, specifically particle removal or pattern damage. The high costs of advanced photomask processes have demanded a zero-defect requirement, a constraint prevalent across the semiconductor industry [8]. The study aims to better understand how process variables affect acoustic performance to establish a process control strategy.
|
