| Image | Part Number | Description / PDF | Quantity | Rfq |
|---|---|---|---|---|
 |
IR (Infineon Technologies) |
3000W DUAL LLC EVAL |
0 |
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IR (Infineon Technologies) |
MOTOR DRIVE REF BOARD |
0 |
|
|
IR (Infineon Technologies) |
BALLAST 32W/T8 120V AC IR21571 |
0 |
|
|
IR (Infineon Technologies) |
BOARD EVAL FOR IRS2336DJ |
0 |
|
|
IR (Infineon Technologies) |
KIT DESIGN CFL BALLAST 120VAC |
0 |
|
|
IR (Infineon Technologies) |
SYSTEM DEVELOPMENT IMOTION CTRLR |
0 |
|
|
IR (Infineon Technologies) |
KIT HALOGIN CONVERTER 100VA |
0 |
|
|
IR (Infineon Technologies) |
DESIGN BOARD FOR IRMCK201 |
0 |
|
|
IR (Infineon Technologies) |
BALLAST UNIV INP FLUOR IR2166 |
0 |
|
|
IR (Infineon Technologies) |
EVAL BALLAST CONTROL |
0 |
|
|
IR (Infineon Technologies) |
BOARD EVAL FOR IRS26310DJ |
0 |
|
|
IR (Infineon Technologies) |
KIT REF DES MOTION CTRL IRMCF312 |
0 |
|
|
IR (Infineon Technologies) |
BOARD EVALUATION ADM8513 |
0 |
|
|
IR (Infineon Technologies) |
EVALUATION KIT DAUGHTER BOARD |
0 |
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IR (Infineon Technologies) |
KIT DESIGN W/IR1176 SYNCH DRIVER |
0 |
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IR (Infineon Technologies) |
BALLAST HI-INT 250W METAL 220VAC |
0 |
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IR (Infineon Technologies) |
DESIGN BOARD FOR IRMCK203 |
0 |
|
|
IR (Infineon Technologies) |
KIT DESIGN CFL BALLAST 220VAC |
0 |
|
|
IR (Infineon Technologies) |
KIT DESIGN IC REF SOFT START |
0 |
|
|
IR (Infineon Technologies) |
KITS DESIGN MOTOR DRIVES/POWER |
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)