| Image | Part Number | Description / PDF | Quantity | Rfq |
|---|---|---|---|---|
 |
Nuvoton Technology Corporation America |
DEMO BOARD FOR NAU85L40YGB |
2 |
|
 |
Nuvoton Technology Corporation America |
AUDIO BASE BOARD + NAU8820YG DAU |
0 |
|
|
Nuvoton Technology Corporation America |
COMPACT AUDIO BASE BRD + NAU8820 |
0 |
|
|
Nuvoton Technology Corporation America |
ISD1900 AUX OUTPUT DEMOBOARD W/ |
0 |
|
|
Nuvoton Technology Corporation America |
AUDIO BASE BRD FOR NAU88XX + NAU |
0 |
|
|
Nuvoton Technology Corporation America |
COMPACT AUDIO BASE BRD + NAU8501 |
0 |
|
|
Nuvoton Technology Corporation America |
COMPACT AUDIO BASE BOARD + NAU88 |
0 |
|
|
Nuvoton Technology Corporation America |
EVAL BRD FOR NAU88L25 INSTALLATI |
0 |
|
|
Nuvoton Technology Corporation America |
ISD91260RI DEVELOPMENT AND DEMO |
0 |
|
|
Nuvoton Technology Corporation America |
ISD15C00 DEVELOPMENT AND DEMO KI |
0 |
|
|
Nuvoton Technology Corporation America |
ISD1900 DEMOBOARD WITH I1964SYI |
0 |
|
|
Nuvoton Technology Corporation America |
THIS PACKAGE IS FURNISHED WITH A |
20 |
|
|
Nuvoton Technology Corporation America |
EVAL BRD FOR NAU88L24 INSTALLATI |
0 |
|
|
Nuvoton Technology Corporation America |
AUDIO BASE BRD FOR NAU88XX + NAU |
0 |
|
|
Nuvoton Technology Corporation America |
ISD1810/06 DEMOBOARD |
0 |
|
|
Nuvoton Technology Corporation America |
AUDIO BASE BOARD + NAU8501YG DAU |
0 |
|
|
Nuvoton Technology Corporation America |
BOARD EVAL FOR ISD2360 |
0 |
|
|
Nuvoton Technology Corporation America |
ISD9160 IOT EVAL MODULE KITS FOR |
0 |
|
|
Nuvoton Technology Corporation America |
COMPACT AUDIO BASE BRD + NAU8810 |
0 |
|
|
Nuvoton Technology Corporation America |
THE EMWIN HMI EVALUATION BOARD ( |
2 |
|
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)