| Image | Part Number | Description / PDF | Quantity | Rfq |
|---|---|---|---|---|
 |
Zetex Semiconductors (Diodes Inc.) |
EVAL BOARD FOR PI7C9X2G312GPB |
2 |
|
 |
Zetex Semiconductors (Diodes Inc.) |
EVAL BOARD FOR PI7C9X2G608GP |
2 |
|
 |
Zetex Semiconductors (Diodes Inc.) |
PI7C9X441 RDK EVAL BOARD |
0 |
|
|
Zetex Semiconductors (Diodes Inc.) |
PI7C9X2G404SLB EVALUATION BOARD |
0 |
|
|
Zetex Semiconductors (Diodes Inc.) |
LOAD SWITCH EVAL |
5 |
|
|
Zetex Semiconductors (Diodes Inc.) |
LOAD SWITCH EVAL |
3 |
|
|
Zetex Semiconductors (Diodes Inc.) |
LOAD SWITCH EVAL |
5 |
|
|
Zetex Semiconductors (Diodes Inc.) |
LOAD SWITCH EVAL |
5 |
|
|
Zetex Semiconductors (Diodes Inc.) |
LOAD SWITCH EVAL |
5 |
|
|
Zetex Semiconductors (Diodes Inc.) |
LOAD SWITCH EVAL |
2 |
|
|
Zetex Semiconductors (Diodes Inc.) |
EVALUATION BOARD FOR PI7C9X2G808 |
1 |
|
|
Zetex Semiconductors (Diodes Inc.) |
LOAD SWITCH EVAL |
5 |
|
|
Zetex Semiconductors (Diodes Inc.) |
PI7C9X2G404SVA EVALUATION BOARD |
2 |
|
|
Zetex Semiconductors (Diodes Inc.) |
LOAD SWITCH EVAL |
5 |
|
|
Zetex Semiconductors (Diodes Inc.) |
EVALUATION KIT FOR PI7C9X2G1616P |
2 |
|
|
Zetex Semiconductors (Diodes Inc.) |
LOAD SWITCH EVAL |
5 |
|
|
Zetex Semiconductors (Diodes Inc.) |
PI7C9X2G304SLB EVALUATION BOARD |
2 |
|
|
Zetex Semiconductors (Diodes Inc.) |
LOAD SWITCH EVAL |
5 |
|
|
Zetex Semiconductors (Diodes Inc.) |
IC INTFACE SPECIALIZED EVAL BRD |
0 |
|
|
Zetex Semiconductors (Diodes Inc.) |
LOAD SWITCH EVAL |
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)