| Image | Part Number | Description / PDF | Quantity | Rfq |
|---|---|---|---|---|
 |
Intersil (Renesas Electronics America) |
BOARD EVALUATION FOR HIP9011 |
0 |
|
|
Intersil (Renesas Electronics America) |
EVAL BOARD FOR ISL5960X |
0 |
|
|
Intersil (Renesas Electronics America) |
EVAL BOARD 1 DFN |
0 |
|
|
Intersil (Renesas Electronics America) |
EVALUATION BOARD FOR ISL6255 |
0 |
|
|
Intersil (Renesas Electronics America) |
ISL9116BIRN-EVZ - EVALUATION BOA |
0 |
|
|
Intersil (Renesas Electronics America) |
EVAL BOARD FOR ISL6146D |
0 |
|
|
Intersil (Renesas Electronics America) |
EVALUATION BOARD FOR ISL54059 |
0 |
|
|
Intersil (Renesas Electronics America) |
EVAL BOARD 1 FOR ISL3331 |
0 |
|
|
Intersil (Renesas Electronics America) |
EVALUATION BOARD ISL88731 |
0 |
|
|
Intersil (Renesas Electronics America) |
EVAL BOARD FOR TW2837-PB1-GE |
1 |
|
|
Intersil (Renesas Electronics America) |
EVAL BOARD FOR ISL6730B |
0 |
|
|
Intersil (Renesas Electronics America) |
EVAL BOARD FOR ISL2109075 |
0 |
|
|
Intersil (Renesas Electronics America) |
ISL32741 EVAL BOARD 1 - 16 PIN S |
0 |
|
|
Intersil (Renesas Electronics America) |
TW8847-EVAL EVALUATION BOARD WIT |
0 |
|
|
Intersil (Renesas Electronics America) |
BOARD EVAL 2.5V VREF ISL21090 |
0 |
|
|
Intersil (Renesas Electronics America) |
EVAL BOARD FOR TW8816-LB3-GR |
0 |
|
|
Intersil (Renesas Electronics America) |
BOARD EVAL FOR ISL21080 |
0 |
|
|
Intersil (Renesas Electronics America) |
EVAL BOARD 2 16LD SOIC |
1 |
|
|
Intersil (Renesas Electronics America) |
EVAL BOARD 1 11X11 BUMP ARRAY WL |
0 |
|
|
Intersil (Renesas Electronics America) |
EVALUATION BOARD FOR ISL5314IN |
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)