asm330lhhx automotive imu

ASM330LHHX – Artificial intelligence applied in automotive sensors


Automotive 6-axis inertial module with embedded machine learning core and dual operating modes

A system-in-package featuring a 3-axis digital accelerometer and a 3-axis digital gyroscope, the ASM330LHHX has an extended temperature range up to +105 °C designed to address automotive non-safety applications.

The ASM330LHHX provides an incredible level of customization thanks to its capability to support up to 16 embedded finite state machines that can be programmed and run independently for motion detection such as vehicle status (stationary or moving), anti-theft alarm, and shock detection.

Key features

15 year longevity
  • Embedded Machine Learning Core and Programmable finite state machine
  • AEC-Q100 qualified
  • Extended temperature range from -40 to +105 °C
  • Embedded compensation for high stability over temperature
  • Supports multi-mode operation, high performance and low power modes
  • Accelerometer user-selectable full scale up to ±16 g
  • Extended gyroscope range from ±125 to ±4000 dps
  • I2C, MIPI I3CSM, and SPI serial interfaces
  • Six-channel synchronized output to enhance accuracy of dead-reckoning algorithms
  • Smart programmable interrupts
  • Embedded 3-Kbyte FIFO available to underload host processor
  • ECOPACK, RoHS and “Green” compliant

Application examples

dead reckoning Dead reckoning (DR)
vehicle to everything v2x Vehicle-to-everything (V2X)
telematics Telematics
car security Anti-theft systems
car accident Impact detection and crash reconstruction
driving Motion-activated functions (adaptive lighting, sensors)
precise positioning Precise positioning
display navigation Display navigation

Recommended resources

steval mki212v1

An effective solution for fast system prototyping and device evaluation directly within your own application, the STEVAL-MKI212V1 provides the complete ASM330LHHX pinout and comes ready-to-use with the required decoupling capacitors on the VDD power supply line.

The adapter is supported by the STEVAL-MKI109V3 motherboard, which includes a high-performance 32-bit microcontroller functioning as a bridge between the sensor and a PC, on which it is possible to use the downloadable graphical user interface (Unico-GUI), or dedicated software routines for customized applications.

mlc examples

Available in ST's public GitHub repository, you will find information, examples and configurations for the Machine Learning Core (MLC), a hardware processing engine dedicated to the most extreme real-time edge computing. The advantage is that it reduces power consumption by moving the processing of certain Machine Learning algorithms from the application processor to the ST sensor.

steval mki109v3

ST's ready-to-use MEMS motherboard (STEVAL-MKI109V3) development platform lets engineers monitor the behavior of ST MEMS sensors, which can help accelerate time to market and maximize the performance of new product designs.

Featuring a high-performance STM32F401VE MCU and flexible power management with software-adjustable power circuitry that allows you to set the sensor supply voltage from 0 to 3.6 V to replicate the required operating conditions.

develop effective ai solutions for monitoring vehicles

Join us for a 1-hour on-demand webinar and learn how to efficiently and effectively monitor a vehicle’s status over long periods of time. Our solution is based on an automotive-grade 6-axis motion sensor with embedded machine learning core that ensures the lowest power consumption and high-accuracy "in-sensor" event detection.

During the webinar, you will learn how to build a unique AI solution delivering best-in-class accuracy, noise and power consumption in the smallest possible size. We will guide you step-by-step through a practical real-life example showing how you can record and process data to create your own AI algorithm.