The accelerated landing of industrial automation has enabled tilt sensing to find rich application scenarios. But designing tilt-sensing applications is challenging, especially when it comes to high-accuracy requirements over a wide temperature range.
The IIS2ICLX is a unique high precision inclinometer capable of seeing, thinking and acting through a Machine Learning Core (MLC). MLC is an efficient way to run AI algorithm algorithms on sensors, saving MCU workload and overall power consumption.
The IIS2ICLX is a 2-axis linear accelerometer with digital output, high accuracy (ultra-low noise, high stability and reliability), and low power consumption. It is capable of providing the application with measured acceleration via an I²C or SPI digital interface.
The IIS2ICLX is housed in a high performance (low stress) ceramic cavity land grid array (CC LGA) package and can operate over a temperature range of -40°C to +105°C. Its temperature variation in zero bias stability is 0.075mg/°C, and the optional range is ±0.5/±1/±2/±3 g, which can maintain high precision and reliability in extreme temperatures and repeated use environments, Particularly suitable for inclination measurement applications.
IIS2ICLX embeds a unique set of features (programmable FSM, machine learning core, sensor hub, FIFO, event decoding and interrupts), which is an enabler for smart and complex sensor nodes, providing high accuracy at very low power consumption and high performance. The sensing unit is fabricated using a dedicated micromachining process developed by STMicroelectronics to produce inertial sensors on silicon wafers.
IIS2ICLX can be used for inclination measurement of various industrial applications, such as: robotic arms, solar panel tracking brackets, antenna pointing, engineering vehicles, bridges, buildings, etc., which are widely used. Its main advantages are:
—— High Performance: Improves Industry Productivity, Reliability, Maintenance, and More
—— Energy saving: MLC uses negligible energy
—— Artificial Intelligence at the Edge: Going from Raw Data to Meaningful Data (Direct Perspective Output)
