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How Ultrasonic Fingerprint Sensing Works and Why it is Important

By Andrew Tweedie 20 August 2019

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 [1] [2] [3] [4]. 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.

Fingerprint Sensing Technology Challenges, Miniaturization and Market.

For some time, the IC design industry has relied on electronic design automation (EDA) and electrical simulation to help achieve first pass success for prototype CMOS devices. The MEMS industry, on the other hand, has never had a codified simulation work stream. As a result, MEMS companies often require expensive and time consuming physical prototype runs to bring ground-breaking new MEMS devices to market. For younger technologies such as MEMS, this process is still in its early stages. Two fundamental technology advancements must be seen through in order to reach the efficiencies of EDA. Those advancements are 1) broad standardization of MEMS manufacturing processes, and 2) the emergence of massively scalable multiphysics simulation tools. Standardization of manufacturing processes is largely driven by market forces, where new automotive, medical, and IoT applications are expected to propel the global MEMS market beyond $30B by 2024. But even with standard processes, designing MEMS is extremely challenging due to multiple, highly-coupled physics processes.

A forecasted 1.6 billion smartphones will be shipped with a fingerprint sensor by 2020. Capacitive sensing has been the dominant technology since Apple introduced it in the iPhone 5s, but ultrasonic sensing technology is poised to disrupt this market now that it can capture fingerprints through a full OLED display stack. This capability is becoming more attractive as there is now an increased number of handsets that are using full edge-to-edge displays. This leaves no room for the traditional home button/fingerprint sensor device that Apple popularized and has since abandoned in its high-end devices. Because of the way they operate, ultrasonic sensing is an excellent match for this technological challenge.


Fingerprint Sensors Technologies

There are 3 main technologies for fingerprint scanners: optical, capacitive and ultrasonic, which are all illustrated in figure 1. In all the fingerprint sensing technologies, when the finger touches the surface of the sensor, the ridges are in contact, while valleys remain at a certain distance. The goal is to implement a sensing system capable of sensing the difference between the valleys and ridges of one’s fingerprint.

Figure 1. Optical (a) [3], capacitive (b) [4] and ultrasound (c) [1] fingerprint scanner schematic.

Figure 1. Optical (a) [3], capacitive (b) [4] and ultrasound (c) [1] fingerprint scanner schematic.

A typical optical fingerprint scanner is depicted in figure 1.a, in which one side of the fingerprint is illuminated through light-emitting diodes (LEDs). Imaging sensors can detect the optical path of the reflected waves. The ridges and valleys are differentiated by the different reflections coming from different parts of a finger [3]. The optical sensors are usually bulkier than other types of fingerprint sensors due to LED arrays. Although, the resolution of the optical fingerprint sensor is usually high compared to its counterparts. The optical method is not considered as high-security fingerprint technology as it can be easily fooled with prosthetics images.

The capacitive method is the most common technology in the fingerprint recognition industry. By measuring the capacitance according to the distance from the chip surface to the finger’s skin, the pattern of a fingerprint, i.e., ridges and valleys, could be obtained [4]. The sensor comprises a lot of micro-capacitors when one’s finger is placed on the chip (figure 1.b), in which, the chip surface is one electrode and the finger skin is the other plate of the capacitors [3]. Small electrical charges are created between the skin and electrodes, and the amount of charges depend on the distance of different parts of the valleys. An op-amp is integrated into the chip to detect the charges. Another important factor for capacitive fingerprint sensor design is the coating layer on the sensor that increases the distance between skin and sensor pixels reducing the capacitance and sensitivity. A thick coating reduces the overall sensitivity of the sensor meaning the patterns can not be captured properly, therefore the coating should be as thin as possible. These sensors are more reliable than optical scanners as different materials are associated with different charges and capacitance, consequently, the sensors can be calibrated for only human fingers and easily detects prosthesis.

To simply put it, an ultrasonic sensor uses sound waves to detect the distance to other objects. Ultrasonic sensors are widely used in everyday applications, including parking-assist sensors for cars, for example. Historically, ultrasonic sensors have been bulkier, consumed more power, and been more expensive than those of competing technologies, which has limited their use in high volume consumer applications. This is changing fast, and new generations of ultrasonic sensors are miniaturized and consume up to an order of magnitude less power. There is no better example than the current development and implementation of ultrasonic fingerprint sensors in smartphones. This technology has been announced several times over the last few years, but a broad release into flagships of the top tier OEM smartphone makers seems imminent.

Ultrasound imaging is the most important application of ultrasound waves, which is normally defined as acoustic waves with a frequency range of more than 20 kHz. Ultrasound imaging systems usually consist of an acoustic wave generator, receiver and the target object. Ultrasound transducers are excited by electrical signals and generates pressure waves, and considering reciprocity, they are capable of sensing reflected acoustic waves. The wave travels at the speed of sound in a medium, which is a characteristic of the material. When a wave travels from one medium to another one, part of the wave’s energy reflects. A reflection of a wave occurs due to the acoustical impedance mismatch of two mediums; acoustic impedance (Z) is defined as density*speed_of_sound. Based on the wave theory for a single frequency acoustic plane wave, the larger difference between medium impedances causes larger reflection factor (RF)

In fact, similar to optical and capacitive sensing that the goal is to differentiate ridges and valleys, the different reflection factor (RF) between glass-skin and glass-air interface creates different reflection wave patterns. These different reflected waves create different electrical signals in Rx transducers that will be translated to fingerprint patterns. Ultrasound systems are implemented in several different ways depending on the sensor area, application resolution, ultrasound loss and power consumption. The chip contains a transducer array comprising several individual elements, each element can be either Tx or Rx or simultaneously both, depends on the design and requirements. Figure 2 describes the functionality of an ultrasonic fingerprint sensor.

Figure 2. Ultrasonic fingerprint technology (Source: OnScale)

Figure 2. Ultrasonic fingerprint technology (Source: OnScale)

Ultrasound sensing has many advantages over the capacitive method, including being insensitive to contamination and moisture on the finger since the ultrasound method is able to image the subsurface of the skin. In addition, ultrasonic waves used in pulse-echo imaging can penetrate the finger’s epidermis, collecting images of sub-surface features [1] providing additional information.

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[1]H. Tang, Y. Lu, J. Jiang, E. Ng, J. Tsai, D. Horsley and B. Boser, “3-D Ultrasonic Fingerprint Sensor-on-a-Chip,” ISSCC, Vol. 51, No. 11, pp. 2522 – 2533, November 2016.
[2]X. Jiang, Y. Lu, H. Tang, J. Tsai, E. Ng, M. Daneman, B. Boser and D. Horsley, “Monolithic ultrasound fingerprint sensor,” Microsyst. Nanoeng. 2017, vol. 3, p. 17059, 2017.
[3]D. Maltoni, D. Maio, A. Jain and S. Prabhakar, Handbook of Fingerprint Recognition, 2nd ed., London: Springer-Verlag , 2009.
[4]J. Lee, D. Min, J. Kim and W. Kim, “A 600-dpi Capacitive Fingerprint Sensor Chip and Image-Synthesis Technique,” IEEE J. Solid-State Circuits, vol. 34, no. 4, pp. 469-475, 1999.
Andrew Tweedie
Andrew Tweedie

Andrew Tweedie is our Founder and UK Director at OnScale. He is a multi-disciplined engineer with 15 years of experience in computer-aided engineering (CAE). Andrew supports EU growth and manages OnScale’s office in Glasgow, Scotland. He holds an Eng.D in Non Destructive Testing from University of Strathclyde.