| Image | Part Number | Description / PDF | Quantity | Rfq |
|---|---|---|---|---|
 |
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD FOR LV8483CS |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL NCP380LSN10 LOAD SW |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL FOR NB7L32MM |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL FOR ADM1021 |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
KIT EVAL AUTO AMIS3062X FAMILY |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
BASEBOARD FOR MOTOR DRIVE |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL NIS6111 FET DIODE |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL FOR ADM1031 |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL NCP380HMUAJ LOAD SW |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
INTEGRATED CIRCUIT |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL NCP1605/1396 |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
BOARD EVALUATION FOR ADT7483 |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL FOR FSB50450 |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
EVAL BOARD FOR LV8774Q |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL NB6L611MNG |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
BOARD EVAL FOR ADT7461 |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
BOARD EVALUATION BBG NBSG16MMN |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
BOARD EVALUATION FOR ADT7475 |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
DAUGHTER BOARD BIP STEP MOTOR |
0 |
|
|
Sanyo Semiconductor/ON Semiconductor |
BASEBOARD FOR MOTOR DRIVE |
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)