| Image | Part Number | Description / PDF | Quantity | Rfq |
|---|---|---|---|---|
 |
IR (Infineon Technologies) |
EVAL BOARD HIGH SIDE SWITCH |
1 |
|
 |
IR (Infineon Technologies) |
3000W DUAL LLC EVAL |
2 |
|
 |
IR (Infineon Technologies) |
EVAL BOARD |
3 |
|
 |
IR (Infineon Technologies) |
EVAL-1ED020I12-B2 TO SHOW THE FU |
1 |
|
 |
IR (Infineon Technologies) |
EVAL BOARD PFC CCM 2500W |
14 |
|
 |
IR (Infineon Technologies) |
KIT H-BRIDGE IFX9201 |
64 |
|
 |
IR (Infineon Technologies) |
LITE LDO SBC BOARD |
2 |
|
 |
IR (Infineon Technologies) |
EVAL BOARD |
2 |
|
 |
IR (Infineon Technologies) |
EVAL 1EDN7550B |
5 |
|
 |
IR (Infineon Technologies) |
DEMO BOARD FOR BTN8962 |
12 |
|
 |
IR (Infineon Technologies) |
IR1167 IR11662 IR11672 DAUGHTER |
2 |
|
 |
IR (Infineon Technologies) |
2EDN7524R DUAL LOW SIDE |
0 |
|
 |
IR (Infineon Technologies) |
EVAL-2ED020I12-F2 TO SHOW THE FU |
3 |
|
 |
IR (Infineon Technologies) |
EVAL MOSFET DRIVER TLE92018 |
3 |
|
 |
IR (Infineon Technologies) |
SPOC+2 MOTHERBOARD |
1 |
|
 |
IR (Infineon Technologies) |
EVAL_2K4W_ACT_BRD_S7 |
1 |
|
 |
IR (Infineon Technologies) |
EVAL 3300W TELECOM/CHARGING |
11 |
|
 |
IR (Infineon Technologies) |
PROFET +2 12V MOTHERBOARD |
1 |
|
 |
IR (Infineon Technologies) |
EVAL MOTHER BOARD W/GD |
12 |
|
 |
IR (Infineon Technologies) |
EVAL-IMM101T-046 IS A STARTER KI |
16 |
|
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)