| Image | Part Number | Description / PDF | Quantity | Rfq |
|---|---|---|---|---|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL FOR BBG NB4N507AD |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL FOR LB11868V |
2 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL NB6L72MMNG |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL FOR LV8732V |
2 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL FOR LV8804FV |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVALUATION NCS2553D |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL FOR LB1930MC |
1 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL FOR NB6N14SMNG |
1 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD NB7L1008MNG |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD DAUGHTER SPI STEP DVR SOIC |
4 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL NCP380HMU15 LOAD SW |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD BIDIR MOTOR DRIVER |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL LOAD SWITCH NCP382 |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL LV8402GP MOTOR DRIVER |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD DAUGHTER LIN STEP DVR SOIC |
2 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL FOR LB1838M |
5 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD NV705143R1DBG |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL FOR LV8741V |
1 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL NCP380HSNAJ LOAD SW |
0 |
|
 |
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL NCS2200A COMP UDFN6 |
5 |
|
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)