Jun 14 — Jul 12, 2022 Virtual

Advanced Modeling Techniques Expand Applications of Picosecond Laser Acoustics for RF Process Monitoring

Johnny Dai

Presentation at China Semiconductor Technology International Conference (CSTIC)
8:00am — 5:00pm

Learn more about the CSTIC's Symposium VI: Metrology, Reliability and Testing session.


Picosecond Ultrasonics (PULSE Technology) is a first principle, rapid non-contact, non-destructive, technology, for the measurement of single layer and multi-layer metal films in semiconductor process control. It has a strong footprint and is uniquely positioned as a tool-of-record for metal film thickness metrology in RF filter monitoring. In addition to thickness measurements, the technique can be used to characterize acoustic velocity values for dielectric and piezoelectric materials which is a critical parameter for process control. We have previously reported on the advantages of PULSE technology for RF applications and its excellent performance to meet the stringent requirements for process monitoring and control.

Most of the RF applications involve multi-layer metal stacks or films on oxide that are more intuitive and are easier to measure and model using our standard modeling algorithms. However, at the measurement wavelength of 532nm, measuring thinner metal films directly on Si substrate is challenging and often requires films on oxide or other films as the signal is complex and dominated by Si oscillations. Another instance when the modeling is challenging is when an SiO2 film is present as a capping layer. The oxide film is included as part of the device stack to obtain a low Thermal Coefficient of the acoustic device. In a typical full stack, when oxide capping layer is present over a multi-layer stack, of oxide/Electrode/Piezoelectric layer/Electrode/Si, PULSE measurement signal is a convolution of SiO2 oscillations and echoes from the multi-layer stacks (most often three or more layers).

To expand the application space of the PULSE technology and improve its performance, we have developed advanced modeling techniques to deconvolution different components of the signal to be able to reliably model parameters of interest. In this paper, we review one of the approaches we have successfully used to improve sensitivity, accuracy, and robustness without impacting repeatability for high volume manufacturing.