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Kionix Productdetail


Tri-Axis, User Selectable ± 2g, 4g, 8g, Digital (SPI) Output, 3x3x0.9mm LGA - KXTIA-1006

The Kionix KXTIA is a low power, high performance tri-axis accelerometer with digital SPI output. The KXTIA features user selectable parameters including 8 or 12 bit modes, g-ranges from +- 2, 4 or 8g, and Output Data Rates from 12.5 Hz to 800 Hz. A 252 byte FIFO/FILO buffer offers programmable watermark interrupt.
The KXTIA features enhanced embedded Screen Rotation, Directional Tap/Double Tap, Orientation detection and Low Power Motion Interrupt. The KXTIA has internal voltage regulators that allow operation from 1.8 V to 3.6 V and is delivered in a 3 x 3 x 0.9 mm, 10-pin, LGA package with an operating temperature range of -40°C to +85°C.

Not Recommended for New Designs.


  • Pin compatible to KXTF9
  • Lower operating and standby power
  • FIFO Buffer
  • Internal voltage regulator

Product Specifications

Axis G Range Sensitivity Noise (µg/√Hz) Resolution Package Size Pins Package Type Interface Output Wakeup Operating Temperature (Min.)[°C] Operating Temperature (Max.)[°C] Supply Voltage [V] Current Consumption
3 2g, 4g, 8g, User-selectable 64, 32, 16 (8-bit), 1024, 512, 256 (12-bit), (counts/g) - 8-bit, 12-bit 3x3x0.9mm 10-pin LGA Digital (SPI) Yes -40 85 1.8-3.6 100-325 μA


EVAL-KXTIA-1006 Cart KXTIA-1006 Evaluation Board KAMMDM001R52 Board Layout (KX_IA)
KAMMDM001R52 Schematic (KX_IA)

Developer Tools

Buy Now Description
IoT Evaluation and Development Kit Cart Kionix's IoT evaluation kit utilizes hardware platform based on Nordic Semiconductor's Bluetooth LE SoC. The compact (27/32mm x 4.2mm), highly integrated modular sensor system integrates Kionix's accelerometer (KX122-1037), combo accel-mag (KMX62-1031), and combo accel-gyro (KXG07) sensors along with ROHM's barometric pressure sensor (BM1383AGLV). Additional sensors can be evaluated using the add-on board that can fit inside the kit. This enables measurement of 3D acceleration, 3D magnetism, 3D rotation, air pressure, and temperature, making it ideal for training, prototyping, and initial set development of IoT and wearable applications.