| Image | Part Number | Description / PDF | Quantity | Rfq |
|---|---|---|---|---|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL LOAD SWITCH NCP382 |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD DAUGHTER LIN STEP DVR NQFP |
2 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD LV3313PMG |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD FOR NCP5106BA36WG |
1 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL LV8413GP MOTOR DRIVER |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD NCN8026AMNG |
2 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL FOR NB6L11SMNG |
1 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL FLEXRAY BUS NCV7381 |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD NCP331SNT1G |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL FOR NCS2220A UDFN6 |
3 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD FOR FUSB307B |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD STK672-440BNG |
1 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD NIS5232-35G |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL FOR LB1909MC |
3 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
UNIVERSAL OFFLINE 200W 4- |
2 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
PCB MODULE WITH LV8548MC |
14 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD LC709203FXE-01 WLCSP9 |
2 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVALUATION NB3H83905 CUST |
1 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
MARS MAX96705 PHY BOARD |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD LV3327PVG |
0 |
|
Evaluation and Demonstration Boards and Kits are hardware platforms designed to facilitate the development, testing, and demonstration of electronic systems. They serve as critical tools for engineers and developers to prototype applications, validate designs, and accelerate time-to-market. These boards integrate processors, sensors, communication interfaces, and software ecosystems, enabling rapid experimentation across diverse industries such as IoT, automotive, and industrial automation.
| Type | Functional Features | Application Examples |
|---|---|---|
| Microcontroller Development Boards | Embedded CPUs, GPIOs, integrated peripherals | IoT devices, robotics |
| FPGA Evaluation Boards | Reconfigurable logic, high-speed interfaces | Communication systems, AI accelerators |
| Sensor Expansion Kits | Multi-sensor integration (temperature, motion, etc.) | Smart agriculture, environmental monitoring |
| Wireless Communication Modules | Bluetooth/Wi-Fi/LoRa protocols, antenna interfaces | Connected healthcare, smart cities |
Typical architecture includes: - Processing Units: Microcontrollers, FPGAs, or SoCs - Memory: RAM, Flash, EEPROM - Interfaces: USB, UART, SPI, I2C, Ethernet - Power Management: Regulators, battery connectors - Software Stack: SDKs, device drivers, IDEs Physical designs often feature standardized form factors (e.g., Arduino Uno, Raspberry Pi HATs) for modular expansion.
| Parameter | Description |
|---|---|
| Processor Performance (MHz/GHz) | Determines computational capability |
| Memory Capacity (RAM/Flash) | Affects program complexity and data storage |
| Interface Types | Dictates peripheral compatibility |
| Power Consumption (mW/MHz) | Critical for battery-operated devices |
| Operating Temperature (-40 C to +85 C) | Defines environmental durability |
- Internet of Things (IoT): Smart home controllers, edge AI nodes - Automotive: ADAS sensor fusion platforms - Industrial Automation: PLC controllers, predictive maintenance systems - Consumer Electronics: Wearables, AR/VR prototypes
| Manufacturer | Representative Products |
|---|---|
| STMicroelectronics | STM32 Nucleo Series, SensorTile Kit |
| Intel | Intel Edison, Movidius Neural Compute Stick |
| Xilinx | Zynq UltraScale+ MPSoC Evaluation Kit |
| Arduino | Arduino MKR Series, Nano 33 IoT |
Key considerations: 1. Match processor capabilities to application complexity 2. Verify interface compatibility with target peripherals 3. Assess software ecosystem maturity (e.g., ROS support) 4. Evaluate power budget requirements 5. Consider long-term availability and community support
- Growing adoption of RISC-V-based evaluation platforms - Integration of AI/ML accelerators in edge computing boards - Expansion of open-source hardware ecosystems - Increased focus on energy-efficient architectures for IoT - Standardization of form factors (e.g., SparkFun's Qwiic system)