Individuals are progressively integrating electrical components into nearly every system possible, thereby imbibing these systems with a degree of intelligence. Nevertheless, to meet the intelligence requirements posed by diverse business applications, especially in healthcare, consumer settings, industrial sectors, and within building environments, there is a growing necessity to incorporate a multitude of sensors.
These sensors now have a common name—IoT or Internet of Things sensors. Typically, these must be of a diverse variety, especially if they are to minimize errors and enhance insights. As sensors gather data through sensor fusion, users build ML or Machine Learning algorithms and AI or Artificial Intelligence around sensor fusion concepts. They do this for many modern applications, which include advanced driver safety and autonomous driving, industrial and worker safety, security, and audience insights.
Other capabilities are also emerging. These include TSN or time-sensitive networking, with high-reliability, low-latency, and network determinism features. These are evident in the latest wireless communication devices conforming to modern standards for Wi-Fi and 5G. To implement these capabilities, it is necessary that sensor modules have ultra-low latency at high Throughput. Without reliable sensor data, it is practically impossible to implement these features.
Turning any sensor into an IoT sensor requires effectively digitizing its output while deploying the sensor alongside communication hardware and placing the combination in a location suitable for gathering useful data. This is the typical use case for sensors in an industrial location, suitable for radar, proximity sensors, and load sensors. In fact, sensors are now tracking assets like autonomous mobile robots working in facilities.
IoT system developers and sensor integrators are under increasing pressure to reduce integration errors through additional processing circuits. Another growing concern is sensor latency. Users are demanding high-resolution data accurate to 100s of nanoseconds, especially in proximity sensor technologies following the high growth of autonomous vehicles and automated robotics.
Such new factors are leading to additional considerations in IoT sensor design. Two key trends in the design of sensors are footprint reduction and enhancing their fusion capabilities. As a result, designers are integrating multiple sensors within a single chip. This is a shift towards a new technology known as SoC or system-on-chip.
Manufacturers are also using MEMS technology for fabricating sensors for position and inertial measurements such as those that gyroscopes and accelerometers use. Although the MEMS technology has the advantage of fabrication in a semiconductor process alongside digital circuits, there are sensors where this technology is not viable.
Magnetic sensors, high-frequency sensors, and others need to use ferromagnetic materials, metastructures, or other exotic semiconductors. Manufacturers are investing substantially towards the development of these sensor technologies using SiP or system-in-package modules with 2D or 2.5D structures, to optimize them for use in constrained spaces and to integrate them to reduce delays.
Considerations for modern sensor design also include efforts to reduce intrinsic errors that affect many sensor types like piezoelectric sensors. Such sensors are often prone to RF interference, magnetic interference, electrical interference, oscillations, vibration, and shock. Designers mitigate the effect of intrinsic errors through additional processing like averaging and windowing.
The above trends are only the tip of the iceberg. There are many other factors influencing the growing sensor design complexity and the need to accommodate better features.
