| Image | Part Number | Description / PDF | Quantity | Rfq |
|---|---|---|---|---|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL NCP1607 100W BOOST |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD STK984-090AG |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL NCN4555 EVAL BRD |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD DAUGHTER SPI STEP DVR NQFP |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD LV8548MCG |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD NCV74715V1G |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL LB1938T MOTOR DRIVER |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL KIT 3 PHASE HYBRID INVERTER |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD DAUGHTER I2C STEP DVR SOIC |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL FOR LV8548M |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD STK672-442BNG |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL LB1973M BRIDGE DRIVER |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVALUATION BBG NBSG14BA |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD FOR NCP349G |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL NCP380HSN10 LOAD SW |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD FOR NCV70627 |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL BBG NBSG72AMN |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL LV8772 MOTOR DRIVER |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
KIT EVALUATION AMIS49587 |
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)