The increasing variety of applications for the Internet of Things is driving predictions that the sales volume for sensors will reach 75 billion units by 2025. Applications unforeseen just five years ago, some of which are described in this article, are requiring sensors to become more intelligent and to achieve ever higher levels of performance. For the foreseeable future, this trend will be seen both for consumer applications (IoT) and industrial applications (IIoT), although generally, the requirements for industrial applications are an order of magnitude more demanding than for consumer applications.
Consumers are now looking for a level of photo quality in slim smart-phones that had earlier only been possible with expensive and bulky DSLR cameras — and this while walking, if not running. To achieve such image quality with the limited optics that can be accommodated in such a tight space, OEMs have deployed Optical Image Stabilization (OIS) and Electronic Image Stabilization (EIS) solutions. The performance of the OIS/EIS components, especially the motion sensor, has a major impact on overall performance. For example, the Google Pixel 2 device, which has a camera with both OIS and EIS, received the highest DXO Mark score from DxOMark Image Labs ever given to a smartphone. The technology that led to that high performance is a motion tracking sensor with extremely low gyroscope and accelerometer noise (<0.004 dps/√Hz and <100 μg/√HZ respectively), sensitivity error within 0.5%, and very tight temperature stability (less than ±0.016%/◦C).
The Variety of Realities: VR, AR, and XR
It is not just cameras that require precise motion sensor performance. Virtual reality (VR), augmented reality (AR), and mixed reality (XR) all need low noise and extremely good temperature stability.
For these applications, the gyroscope noise requirement is similar to that for OIS, (<0.004 dps/√Hz), but the accelerometer noise has to be even lower, (<75 μg/√HZ). Other key parameters are a very low sensitivity error (<0.5%) for both gyroscope and accelerometer, and also good temperature stability (<10 mdps/~C) for the gyroscope. Another system-level feature that is beneficial for VR/AR/XR applications is the ability to synchronize the various sensors to a ppm-level (0.0001%) accuracy system clock, so that timing errors stemming from less accurate internal oscillator clocks (1% clock error for PLL) can be avoided in applications that depend on precise timing.
VR stand-alone head-mounted devices (HMD) especially, need good temperature linearity and stability, as well as low hysteresis and noise, to provide the best possible user experience. For the very high precision required by VR and AR controllers, sensors also need high resolution to be able to accurately handle the slow and precise movements of a hand.
Dead-reckoning systems are used for navigation in both consumer and commercial applications. They provide location data in the absence of GNSS/GPS signals by means of inertial measurement units (IMU), which use linear acceleration information derived from accelerometers, and angular rotation rate from gyroscopes, to calculate altitude, angular rate, linear velocity, and position. The accuracy of these measurements is critical because errors are additive — they increase over time. For high-precision navigation, a gyroscope needs to have extremely low gyro offset for temperature (between 3 to 5 mdps/°C) and low gyro noise density (below 4 mdps/√Hz).
A promising consumer application for dead-reckoning navigation, is to sense one's location inside a shopping mall. It is particularly challenging to sense the floor someone is on. It would allow users to know which shops are near them. But more importantly, it could save time and lives by providing emergency services with the specific floor and location where the mobile caller requested help, rather than searching floor by floor. This can be achieved with high-precision, low-noise pressure sensors that can measure individual stairs. This is now achievable because pressure sensors are available that can go as low as one pascal, or about 10 cm, in precision.
Predictive maintenance (PdM) is one of the most important applications for sensors in the IIoT. It is a system in which machine and other processing parameters are continuously monitored. Managers can therefore have an ongoing picture of machine health throughout a factory, enabling them to schedule maintenance when data indicates there might be an impending problem. This is an alternative to waiting for a failure to occur or shutting down at regular intervals whether or not it is necessary.
Continuous online monitoring can provide benefits such as reducing downtime by as much as 70%, extending motor lifetimes by up to 30%, and reducing energy consumption by up to 10%. Although these types of sensors are now being integrated into new production equipment, they can also be added to older equipment.
Another example is driverless agricultural tractors. In addition to the other requirements for autonomous vehicles, tractors have to deal with extreme vibration, which can cause navigational errors because of its effects on the motion tracking sensors. The sensors in the precision navigation system therefore require vibration isolation — bias stability of below 2°/hr for gyroscopes and below 10 μg for accelerometers, an angle random walk (ARW) of 0.084°/√hr, and velocity random walk (VRW) of 0.016 m/s/√hr. It is also important to have very small misalignment and mounting errors — less than 0.05° for both.
Microphones are increasingly deployed in home automation. For example, smart speakers have multiple microphones — the Amazon Echo has seven. You can easily imagine a future where a family has more than 100 microphones inside their home. These would have to be high performance, with a signal-to-noise-ratio (SNR) of more than 70 dB, while having an acoustic overload point (AOP) of more than 120 dB. One device that meets these specs is the InvenSense ICS-40730 microphone, which has a 74 dB SNR and 123 dB AOP. This allows the microphone to listen to everything happening near the smart speaker even in the presence of loud noises, like a door closing too hard, or a TV playing an action movie too close to the speaker.
Range-finding or time-of-flight (ToF) sensors with improved performance are starting to address the requirements of machines that move, such as self-driving cars, service robots, and industrial monitoring drones. For them to be able to “see” their surroundings precisely and avoid obstacles, they need very precise sensors. For example, ultrasonic-based sensors are now able to measure distances up to five meters very precisely.
However, different sensor parameters are important for different applications. For example, drones care more about bias, time, and temperature stability, while house-cleaning robots care mostly about bias stability and noise.
Improved ultrasonic-based sensors can also enhance access control systems. For example, they enable fingerprint sensors that can work well even under metal or plastic, allowing for operation in challenging environments. These ultrasonic solutions can meet the needs of access control: a false acceptance rate (FAR — giving access to an unauthorized person) of at least one in 50,000 and false rejection rate (FRR — not giving access to an authorized person) of less than one in 50.
These applications are only the tip of the iceberg for sensor applications in the rapidly growing world of interconnectivity. There is a push-pull relationship between sensor performance and applications. The proliferation of new and improving applications drives the birth of new sensor technologies and the constant improvement of existing sensor performance. At the same time the availability of new types of sensors and higher levels of performance can suggest applications that weren't possible before. These two trends will be the engine to drive the ever expanding IoT and IIoT connected world.