STMicroelectronics has introduced the new ISM330ISN always-on 6-axis inertial measurement unit (IMU) that is designed for movement and position sensing and uses its embedded intelligence to deliver unrivaled performance and accuracy for its size and power. Ideal for IoT and industrial applications, this new IMU sets to accelerate response time and extend battery life in equipment such as condition monitors for predictive maintenance, as well as battery-operated asset trackers and industrial applications such as robots.
The ISM330ISN is part of ST’s iNEMO family of IMUs and contains a 3-axis accelerometer and 3-axis gyroscope with low-noise sensing performance, and an output data rate (ODR) of 6.6kHz. With the ISPU, the sensor ensures consistently high accuracy while consuming only 0.59mA in combination mode with the accelerometer and gyroscope active. The intelligence built into this IMU enables smart devices to perform advanced motion-detection functions in the sensor without interaction with the external microcontroller, thus saving power at system level.
Key Features
- 3-axis accelerometer with selectable full scale: ±2/±4/±8/±16 g
- 3-axis gyroscope with selectable full scale: ±125/±250/±500/±1000/±2000 dps
- Embedded ISPU: ultra-low-power, high-performance programmable core to execute signal processing and AI algorithms in the edge for a seamless digital-life experience
- Low-power consumption: 0.59 mA in high-performance mode, 0.46 mA in low-power mode (gyroscope + accelerometer only, ISPU not included)
- Low noise: 70 μg/√Hz in high-performance mode
- Sensor hub feature to efficiently collect data from additional external sensors (up to 4 external sensors)
- SPI / I²C serial interface
- Analog supply voltage: 1.71 V to 3.6 V with independent IO supply (1.62 V)
ISPU architecture based on digital signal processing (DSP), is extremely compact and power-efficient, with 40Kbytes of RAM and occupying just 8000 gates on the sensor die. Performing floating-point operations with single-bit precision, ISPU is ideal for machine-learning applications and binary neural networks.
