| Image | Part Number | Description / PDF | Quantity | Rfq |
|---|---|---|---|---|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD NCP374MU075 |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
NIV6350MT2 EVAL BOARD |
1 |
|
|
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD NCN512110 |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL 2 MP CO-PROCESSOR HB |
1 |
|
|
Sanyo Semiconductor/ON Semiconductor |
EVAL KIT CLOCK GEN PROGR |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL 1 MP CO-PROCESSOR HB |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
EVAL DUAL MODE NCP45491 |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
EVAL BRIDGE DR LV8549 |
3 |
|
|
Sanyo Semiconductor/ON Semiconductor |
EVAL BIDIRECT MOTOR DR LB1848MC |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
EVALUATION BOARD FOR LC70 |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
EVAL IPM MOTOR DRIVE |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
EVAL 200W PFC |
4 |
|
|
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD NCP45520IMN |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
EVALUATION KIT FOR LV8121 |
2 |
|
|
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD FOR NCN512010 |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD FOR NCN511010 |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD NCP333FCT2G |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL DIFF LVPECL FANOUT |
1 |
|
|
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD LV88563 |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL KIT 3 PHASE HYBRID INVERTER |
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)