| Image | Part Number | Description / PDF | Quantity | Rfq |
|---|---|---|---|---|
 |
Canon |
120MXSM EVAL KIT |
1 |
|
 |
Texas Instruments |
ACCELEROMETER MEASUREMENT TOOL |
35 |
|
 |
Senseair |
SUNRISE SADK |
9 |
|
 |
Senseair |
K30 SADK |
4 |
|
 |
Texas Instruments |
EVAL MODULE FOR LMT88-9 |
1 |
|
 |
Roving Networks / Microchip Technology |
DEV KIT TOUCH SCREEN 7" 4-WIRE |
1 |
|
 |
NXP Semiconductors |
EVB KIT FRDM-K64F/FRDM-STBCAGM01 |
113 |
|
 |
KEMET |
GAS DETECTOR, EVALUATION KIT DIG |
5 |
|
 |
ROHM Semiconductor |
KX224-1053 EVAL BOARD |
0 |
|
 |
Pololu Corporation |
QTRX-MD-05RC REF ARRAY 5CHNL |
125 |
|
 |
MikroElektronika |
BOARD MOTION SENSOR AMN11112 |
0 |
|
 |
Melexis |
BOARD EVAL FOR MLX90614 |
17 |
|
 |
Bosch Sensortec |
EVAL BOARD FOR BMP388 |
11 |
|
 |
Analog Devices, Inc. |
EVAL BOARD FOR CN0343 |
1 |
|
 |
STMicroelectronics |
EVAL BOARD FOR IIS328DQ |
0 |
|
 |
Allegro MicroSystems |
EVAL BOARD FOR ACS733 |
2 |
|
 |
NXP Semiconductors |
DATA COLLECTION REFERENCE DESIGN |
22 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL 2 MP 1/6 CIS 54CSP 28 |
0 |
|
 |
Analog Devices, Inc. |
BOARD BREAKOUT FOR ADXL344Z |
0 |
|
 |
BECOM Systems GmbH |
KIT EVAL 3D SENSOR TOF M100 |
0 |
|
Evaluation boards for sensors are specialized hardware platforms designed to test, validate, and develop sensor-based applications. These boards integrate sensor elements with processing units, communication interfaces, and power management modules. They play a critical role in accelerating product development cycles in industries such as IoT, industrial automation, healthcare, and consumer electronics by enabling rapid prototyping and performance characterization.
| Type | Functional Features | Application Examples |
|---|---|---|
| Temperature Sensor Boards | High-precision thermal sensing with digital/analog outputs | Climate control systems, medical devices |
| Accelerometer Boards | 3-axis motion detection with programmable sensitivity | Vibration monitoring, fitness trackers |
| Pressure Sensor Boards | Atmospheric/differential pressure measurement | Weather stations, automotive systems |
| Environmental Sensor Boards | Multi-parameter detection (humidity, gas, light) | Smart agriculture, air quality monitors |
| Image Sensor Boards | High-resolution optical sensing with ISP integration | Surveillance cameras, machine vision |
Typical evaluation boards consist of: - Sensor element (MEMS, CMOS, or discrete transducers) - Microcontroller/SoC with ADC/DAC interfaces - Communication modules (I2C, SPI, UART, BLE/Wi-Fi) - Power management ICs and voltage regulators - Debugging interfaces (JTAG, SWD) - Auxiliary components (LED indicators, potentiometers) The PCB layout optimizes signal integrity while minimizing electromagnetic interference.
| Parameter | Description | Importance |
|---|---|---|
| Measurement Range | Minimum/maximum detectable values | Determines application suitability |
| Accuracy | Error margin vs. reference values | Impacts system reliability |
| Sampling Rate | Data acquisition frequency | Defines dynamic response capability |
| Power Consumption | Operating current/voltage requirements | Affects battery life and thermal design |
| Interface Type | Communication protocol compatibility | Dictates system integration complexity |
| Manufacturer | Representative Product | Key Features |
|---|---|---|
| STMicroelectronics | STEVAL-MKI187V1 | 6-axis IMU with advanced calibration |
| Texas Instruments | BOOSTXL-ULTRASONIC | Ultrasonic sensing for distance measurement |
| Analog Devices | EVAL-ADICUP3029 | Low-power Cortex-M4F based platform |
| NXP Semiconductors | FRDM-FXS-MULTI-B | Multi-sensor fusion for IoT applications |
Key considerations include: - Match sensor specifications to target application requirements - Verify compatibility with existing development ecosystems - Evaluate power budget and form factor constraints - Consider available software support (drivers, SDKs) - Assess calibration and certification requirements Example: For a wearable health monitor, prioritize low-power accelerometers with medical-grade accuracy.
Emerging trends include: - Integration of AI accelerators for edge computing - Development of wireless sensor nodes with energy harvesting - Advancements in MEMS fabrication for higher sensitivity - Standardization of sensor fusion algorithms - Growth of open-source hardware ecosystems Market projections indicate a 12% CAGR through 2027 driven by IoT and Industry 4.0 adoption.