| Image | Part Number | Description / PDF | Quantity | Rfq |
|---|---|---|---|---|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD NV70501R10DB |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD NCV7471A 250MA |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL 1 MP CO-PROCESSOR HB |
2 |
|
|
Sanyo Semiconductor/ON Semiconductor |
EVAL SINGLE MODE NCP45491 |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
EVAL MOTOR DR LV88564 |
3 |
|
|
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL 1 MP CO-PROCESSOR HB |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
DEV KIT X-CEL XGS12000 MONO |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL 1 MP CO-PROCESSOR HB |
3 |
|
|
Sanyo Semiconductor/ON Semiconductor |
EVAL BRD NCV7748 |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
NCP4306 HIGH EFFICIENCY SR EVALU |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
EVALUATION BOARD FOR LV88 |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD IMAGE SENSOR |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
NIV6350MT1 EVAL BOARD |
1 |
|
|
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD NIS5431 |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL 1 MP CO-PROCESSOR DEM |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
NCP4306 HIGH EFFICIENCY SR EVALU |
4 |
|
|
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL NCP380LSN05 LOAD SW |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
EVABOARD FOR LV8806QA |
2 |
|
|
Sanyo Semiconductor/ON Semiconductor |
EVAL MOTOR DR LV88552 |
3 |
|
|
Sanyo Semiconductor/ON Semiconductor |
DEMO KIT FOR NB7VPQ702MMU |
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)