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In this article, we will discuss the issues the MEMS industry faces with packaging and how OnScale can be used to simulate a wide range of packaging effects.
Traditional high data rate communication realized by electric components – such as A/D converters, encoders, modulators, amplifiers, processors and similar integrated circuits – faces a challenge as further increase of frequency would result in cooling problems due to the generated amount of thermal energy in the form of Joule heating. An alternative approach is light-based information transfer in guided structures, such as optical fibers. However, optical fibers occupy relatively large spaces due to the limited minimum size of bends allowed by the small refractive index difference between the wave guiding core and the outer cladding.
In our previous two blog posts, How Ultrasonic Fingerprint Sensing Works and Why it is Important and Which transducer type is best for ultrasonic fingerprint sensing: CMUT, PMUT or PZT?, we explained the fingerprint sensing principles and the different ultrasound transducers. Micromachined ultrasound transducers (MUTs) can be fabricated in a small size in an array suitable for relatively large frequency applications compared to bulk piezoelectric transducers, and are then suitable to perform beamforming to generate ultrasound images. Piezoelectric micromachined ultrasonic transducers (PMUTs) are a better candidate for fingerprint sensing compared to CMUTs, though there is no need for DC bias voltage for both Tx and Rx operation. In fact, PMUTs AC only working regime reduces the charging effect in the dielectric/piezo improving the reliability of the device.
In this article, we discuss how to perform a Monte Carlo Simulation (MCS) on a PMUT ultrasonic sensor in OnScale to obtain a full picture of the design space.
Ultrasound Transducers: Modalities and Operation During the last several decades, ultrasound devices have become ubiquitous in daily life for various airborne and immersion applications such as automobile, parking sensors, and medical imaging. Traditional piezoelectric transducers were previously used mainly in ultrasound applications however, in the past two decades, micromachined ultrasound transducers (MUTs) have been developed and used in several medical imaging and consumer electronics applications such as handheld/catheter-based medical devices and fingerprint sensors. In general, MUTs operate in 2 different mechanisms, capacitive force (CMUT) or piezoelectric (PMUT) sensing-actuation. See figure 1 and ref.  .
Human fingerprints are detailed, unique and more importantly, invariant over time, making them useful and reliable markers of human identity. Fingerprint sensors are used to capture an image of a human fingerprint, and can be realized through different technologies such as optical, capacitive and acoustic mechanisms    . Ultrasonic sensing has started to make headway into much wider applications as new ultrasonic transducer technologies have reduced the power, size, and cost of the technology. With significant use in the medical and industrial markets, consumer electronics is also starting to adopt this technology.
In the rapidly developing world of Internet of Things (IoT), the radio frequency front-end (RFFE) of smart devices will have to handle higher data rates and access the full bandwidth of 4G/5G wireless technology. The reason for this, of course, is the growing demands of ubiquitous low latency data at higher operating frequencies required to accommodate enhanced data transmission capabilities and rapidly growing numbers of users.
Sensors are all around us. There are a dozen or so in your smartphone, and dozens more in your laptop, tablet, TV remote control and drone. There are perhaps hundreds in your car, thousands on commercial aircraft and reusable rockets, that will one day take us to the moon, mars, and beyond...
In this article, we will discuss how to understand and visualize correctly the results coming out of a PMUT ultrasonic sensor simulation in the OnScale Post-processing Mode.
Introduction The capability to do rapid design and virtual prototyping of Internet of Things (IoT) edge devices is a competitive advantage for companies seeking to make a big impact in emerging markets with as little up-front investment and risk as possible. Currently, this type of risk is mitigated by prototyping – an iterative process that can cost from $100k to $1M per foundry run depending on the complexity of the process. In addition, prototyping is time consuming and can significantly increase time-to-market. Simulation that can handle large-scale, real-world problems can reduce the number of prototyping iterations required, reducing cost, time-to-market, and mitigating the risks that come with developing new technology.