Paving The Way For 5G: RF Filter Process Monitoring
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2022-05-16
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Paving The Way For 5G: RF Filter Process Monitoring And Control Using Picosecond Ultrasonic Metrology
A recent study shows the radio frequency (RF) filter market growing steadily by nearly $16 billion from 2019 to 2024 at a compound annual growth rate (CAGR) of approximately 20%, according to Technavio. The strong growth in the RF filter market is driven by the increased adoption of 5G technology, the surge in smartphones using 5G, and commercial and consumer devices dependent on internet of things (IoT) applications. Together, these factors are some of the most significant players driving society’s digital transformation.However, the RF filter market is faced with many of the same challenges the semiconductor industry as a whole is experiencing, including the need to pack more into increasingly smaller spaces. In each successive generation of RF filters, the number of filters has not only steadily increased, the rising number of filters has led to a need for more stringent process monitoring and control. A frequency accuracy, 3σ of 0.1%, requires film thickness control within the same accuracy or better.Let’s look at one RF filter component, a bulk acoustic wave (BAW) resonator. A BAW is a piezoelectric structure sandwiched between the top and bottom electrodes. The resonant frequency depends on the acoustic velocity and the thickness of the piezoelectric film, and the thickness of the electrode. The thickness of the top electrode as a mass loading layer can be dialed in to generate a frequency shift, which is often used to form a filter passband.Since the RF filter process is directly correlated to thickness, extremely uniform films (~0.1% or better) need to be deposited. With the additional requirements of 5G to support higher frequencies and increased bandwidth, RF filter device manufacturers employ several different process knobs to tune the devices. For example, we see an increasing trend toward thinner layers to support higher frequencies, the adoption of Sc-doped piezoelectric materials to improve piezoelectric coupling, and the addition of temperature compensation SiO2 layers to the stack to improve the temperature coefficient of the resonator.For more than 20 years, picosecond ultrasonic technology has been a metrology workhorse for in-line metal films in multiple end-market segments. Critical process steps in the RF filter process flow (i.e., characterizing the acoustic velocity of the piezoelectric layer) that measure multi-layer metal stacks of repeating metal films are served exclusively by picosecond ultrasonic technology. Feeding data forward from the picosecond ultrasonic measurements to the trimming operation in an advanced process control loop achieves the required cross-wafer uniformity and is a key enabler for tuning the filter performance.Since picosecond ultrasonic technology plays such a critical part in the process, constant improvements to hardware and algorithms should be made to stay one step ahead of the needs of device manufacturers.When measuring a multi-layer stack, for example, an oxide/top electrode/piezo/bottom electrode, it is extremely critical to measure all four layers simultaneously. The measurement signal in this case is a convolution of SiO2 oscillations and echoes from the multi-layer stacks (blue curve in figure 4). Advanced modeling techniques using picosecond ultrasonic technology address the deconvolution of different components of the signal, improving sensitivity, accuracy, repeatability, and robustness requirements that are needed for in-line high-volume manufacturing. After subtracting the thermal background (green curve in figure 4), and using one such advanced processing technique, the relevant features in the signal are decoupled from the oscillations (red curve in figure 4). This allows all four layers of the stack to be simultaneously modeled. When measuring such stacks, the typical repeatability is 3σ<0.3%, and the fleet is intrinsically matched.Picosecond ultrasonic technology has become a unique solution for specific challenges in the RF process monitoring space. The sensitivity and repeatability offered by these modeling techniques will enable fabs to meet the demands of customers depending upon RF filters to power 5G technology and the internet of things.
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