| Image | Part Number | Description / PDF | Quantity | Rfq |
|---|---|---|---|---|
 |
Aceinna Inc. |
EVAL BOARD FOR MCA1101-20-3 |
5 |
|
 |
GroupGets |
PTMINI FLIR LEPTON SMART MODULE |
163 |
|
 |
ams |
BOARD ADAPTER AS5147 |
26 |
|
 |
Maxim Integrated |
EVAL KIT FOR MAX6581 |
111 |
|
 |
TDK InvenSense |
ICM-20601 EVALUATION BOARD |
1 |
|
 |
STMicroelectronics |
BOARD ADAPTER ACCEL LIS2DH12 |
0 |
|
 |
DFRobot |
10 DOF MEMS IMU SENSOR |
17 |
|
 |
Texas Instruments |
EVAL BOARD FOR TMP235 |
3 |
|
 |
NVE Corporation |
KIT EVALUATION GT SENSOR |
5 |
|
 |
Texas Instruments |
EVAL BOARD FOR TMP112 |
3 |
|
 |
ams |
BOARD DEMO AS5040 |
1 |
|
 |
Analog Devices, Inc. |
BOARD EVAL FOR AD7156 |
1 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL 2MP 1/3 CIS RGB-IR 12 |
2 |
|
 |
Texas Instruments |
EVAL BOARD FOR OPT3002 |
12 |
|
 |
Allegro MicroSystems |
EVAL BOARD FOR ACS730 |
2 |
|
 |
STMicroelectronics |
BOARD & REF DESIGN |
0 |
|
 |
ROHM Semiconductor |
BOARD EVALUATION FOR KXCNL-1010 |
0 |
|
 |
STMicroelectronics |
MEMS MOTION SENSOR EVAL BOARDS |
97 |
|
 |
Analog Devices, Inc. |
EVAL BOARD FOR ADXRS290Z-S |
0 |
|
 |
Analog Devices, Inc. |
EVAL BOARD FOR ADPD2214 |
1 |
|
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.