| Image | Part Number | Description / PDF | Quantity | Rfq |
|---|---|---|---|---|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD FOR NCP1095 POE-PD |
7 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL FOR LV8760T |
4 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL NB7V33M |
1 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD NCV74204V1G |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD NCP383LMUAJAG |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD DAUGHTER SPI STEP DVR NQFP |
1 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD DAUGHTER I2C STEP DVR SOIC |
4 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD FOR NBC124XX |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
MOTOR DRIVER SOLUTION KIT FOR LV |
2 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD NCP382LMN15AG |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL FOR LV8731V |
4 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD FOR NCV70627 |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD NCP457FCT2G |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD NB3W1200LMNG |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL FOR LV8498CT |
2 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL NCP380HMU10 LOAD SW |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD NCV7718G |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL 2 MP CO-PROCESSOR HB |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL MOTOR DR LV88554 |
3 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL FOR LB1973JA |
3 |
|
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)