| Image | Part Number | Description / PDF | Quantity | Rfq |
|---|---|---|---|---|
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IR (Infineon Technologies) |
EVAL MICROPHONE IM69D120V01 |
0 |
|
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IR (Infineon Technologies) |
2500W CCM POWER FACTOR CORRECT |
0 |
|
 |
IR (Infineon Technologies) |
KIT DESIGN BALLAST IRS2573D |
0 |
|
 |
IR (Infineon Technologies) |
BOARD EVAL FOR IR1155S |
0 |
|
 |
IR (Infineon Technologies) |
KIT EVAL ORING DEMO BOARD/IR5001 |
0 |
|
 |
IR (Infineon Technologies) |
DEMO BOARD TLE7181 |
0 |
|
 |
IR (Infineon Technologies) |
BOARD DEMO FOR TLE 7209-2R |
0 |
|
 |
IR (Infineon Technologies) |
BOARD EVALUATION ADM6996FC |
0 |
|
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IR (Infineon Technologies) |
KIT DESIGN EVAL BOARD IR2214SS |
0 |
|
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IR (Infineon Technologies) |
800W POWER FACTOR CORRECT EVAL |
0 |
|
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IR (Infineon Technologies) |
DESIGN KIT 1/4 HP DC FOR IR31XX |
0 |
|
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IR (Infineon Technologies) |
BOARD DEMO 300/500W CCM-PFC SMPS |
0 |
|
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IR (Infineon Technologies) |
BOARD EVALUATION ADM7001 |
0 |
|
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IR (Infineon Technologies) |
BOARD DEMO 160W DCM-PFC SMPS |
0 |
|
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IR (Infineon Technologies) |
KIT DESIGN EVAL BOARD/IR22141SS |
0 |
|
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IR (Infineon Technologies) |
KIT DESIGN PENTIUM II |
0 |
|
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IR (Infineon Technologies) |
IC BALLAST DIMMING CFL 3-WAY |
0 |
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IR (Infineon Technologies) |
BALLAST DIM DIG 32W/T8 120VAC |
0 |
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IR (Infineon Technologies) |
KITS DESIGN MOTOR DRIVES/POWER |
0 |
|
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IR (Infineon Technologies) |
BALLAST PFC INTEGR 36W/T8 230VAC |
0 |
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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)