The biometrics arms race and the future of in-surface fingerprint sensors

This is a guest post by Ian Campbell, CEO of OnScale.

What if your fingerprint was able to unlock not only your smartphone, but also your front door, your car, and more? Not only would you never forget your keys again, but the way you interact with your things within the IoT would completely change. Recent advancements in fingerprint sensor technologies will enable these capabilities and much more.

Ultrasonic fingerprint sensor technology based on Piezoelectric Micromachined Ultrasonic Transducers (PMUTs) will make leaps and bounds in the coming years and could show up as early as later this year in devices like the Samsung Galaxy S10 and its rumored under-display ultrasonic fingerprint sensor. Moving forward, ultrasonic fingerprint sensors will become the predominant biometric sensing modality in the multi-billion dollar fingerprint sensing market due to the technology’s accuracy, ability to sense through solid objects like an edge-to-edge smartphone display, ability to sense wet fingers on wet surfaces, and “fool proof” ability to sense 3D transdermal fingerprint images. For these reasons, ultrasonic fingerprint sensors will displace older fingerprint sensing technologies in applications like smartphones, laptops, wearables, automotive, and many other IoT devices.

However, this promised future of biometrics will only come as quickly as engineers are able to perfect the technology and ready it for mass-market adoption. Traditional methods of engineering new technologies that rely on physical prototyping simply won’t cut it for ultrasonic fingerprint sensors. For example, in smartphone applications, engineers must not only prototype the fingerprint sensor itself but must also prototype thousands of smartphones with the fingerprint sensor installed. Only then can they collect enough data to train very complex AI algorithms for fingerprint recognition. This “learning by prototyping” approach is costly, time-intensive, and extremely risky. The best engineers in the world at marketing-leading smartphone companies and market-leading fingerprint sensor companies weren’t able to get ultrasonic fingerprint sensors into the current generation of smartphones with edge-to-edge displays. Without new Cloud CAE engineering methods, they won’t be able to get ultrasonic fingerprint sensors into the next generation either.

Revolutions in new technology require revolutions in engineering. The solution to producing ultrasonic fingerprint sensor solutions at the competitive rate required by industries like the smartphone industry is on-demand, scalable Cloud computer-aided engineering (CAE). With Cloud CAE, engineers can rapidly optimize ultrasonic fingerprint sensor designs in full 3D, test the designs virtually in 3D smartphone touch-display stackups, simulate thousands of permutations of sensor and system mechanical tolerances, simulate external effects like temperature changes and warping of materials, and quickly collect AI algorithm data from thousands of virtual prototypes without ever building a physical prototype. With Cloud CAE, engineers will be able to rapidly introduce new biometric sensors like ultrasonic fingerprint sensors in many applications we can only dream about today.

The biometrics arms race will ultimately be defined by those designers and manufacturers pairing the best and fastest simulation and virtual prototyping technology with their innovative ideas. The firms that adopt Cloud CAE will become dominant in their fields.

About the author

Ian Campbell is a venture-backed Silicon Valley CEO and expert in MEMS and semiconductor technology.

DISCLAIMER: BiometricUpdate.com blogs are submitted content. The views expressed in this blog are that of the author, and don’t necessarily reflect the views of BiometricUpdate.com.

Article Topics

access control  |  authentication  |  biometrics  |  cloud services  |  fingerprint sensors  |  OnScale