| Image | Part Number | Description / PDF | Quantity | Rfq |
|---|---|---|---|---|
 |
Melexis |
EVAL BOARD FOR MLX90640 |
49 |
|
 |
Texas Instruments |
EVAL BOARD DRV5011 DRV5012 |
2 |
|
 |
Roving Networks / Microchip Technology |
AT42QT1010 EVALUATION KIT |
12 |
|
 |
Allegro MicroSystems |
BOARD DEMO 759LCB-050B SENSOR |
1 |
|
 |
Texas Instruments |
EVAL MODULE FOR TMP709 |
1 |
|
 |
Analog Devices, Inc. |
EVAL BOARD FOR ADIS16475-1 |
2 |
|
 |
Silicon Labs |
BOARD UDP PLUG-IN CARD SI7005 |
2 |
|
 |
Adafruit |
ADAFRUIT MPRLS PORTED PRESSURE S |
328 |
|
 |
Analog Devices, Inc. |
BOARD EVAL FOR ADXL345 |
1 |
|
 |
Analog Devices, Inc. |
EVAL BOARD FOR ADPD2212 |
1 |
|
 |
Basler |
DEV KIT DAA2500-14LC-MZ7010 |
0 |
|
 |
KEMET |
GAS DETECTOR, EVALUATION KIT, AN |
3 |
|
 |
Texas Instruments |
EVALUATION MODULE TMP107 |
5 |
|
 |
Neonode |
EVAL KIT MODULE 90 DEG 158MM |
185 |
|
 |
Silicon Labs |
EVAL BOARD FOR SI7050 |
1 |
|
 |
STMicroelectronics |
LIS2MDL ADAPTER BOARD STANDARD D |
61 |
|
 |
STMicroelectronics |
BOARD EVAL FOR LIS3DSH |
10 |
|
 |
TOKO / Murata |
PYROELECTRIC INFRARED SENSOR EVA |
0 |
|
 |
Analog Devices, Inc. |
BOARD EVALUATION FOR AD7746 |
42 |
|
 |
Bosch Sensortec |
EVAL BOARD FOR BMP390L |
77 |
|
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.