| Image | Part Number | Description / PDF | Quantity | Rfq |
|---|---|---|---|---|
 |
Intersil (Renesas Electronics America) |
EVALUATION BOARD FOR ISL6256A |
0 |
|
|
Intersil (Renesas Electronics America) |
BOARD EVAL FOR ISL6132 |
0 |
|
|
Intersil (Renesas Electronics America) |
EVAL BOARD 1 16 PIN QSOP |
0 |
|
|
Intersil (Renesas Electronics America) |
EVAL BOARD FOR ISL68137-61P |
0 |
|
|
Intersil (Renesas Electronics America) |
ISL79985-WANT FOR FREESCALE I.MX |
0 |
|
|
Intersil (Renesas Electronics America) |
DEMO BOARD HIP2103 HIP2104 |
12 |
|
|
Intersil (Renesas Electronics America) |
BOARD EVAL FOR ISL5426A MUX |
0 |
|
|
Intersil (Renesas Electronics America) |
EVALUATION BOARD FOR ISL55110 |
2 |
|
|
Intersil (Renesas Electronics America) |
EVALUATION BOARD FOR ISL6119 |
0 |
|
|
Intersil (Renesas Electronics America) |
EVAL BOARD FOR ISL22102IV20Z |
0 |
|
|
Intersil (Renesas Electronics America) |
EVAL BOARD FOR TW9966AT |
0 |
|
|
Intersil (Renesas Electronics America) |
ISL32740 EVAL BOARD 2 - 16 PIN Q |
0 |
|
|
Intersil (Renesas Electronics America) |
EVALUATION BOARD FOR ISL88042 |
0 |
|
|
Intersil (Renesas Electronics America) |
EVAL BOARD WITH MIPI INTERFACE |
0 |
|
|
Intersil (Renesas Electronics America) |
EVAL BOARD WITH BT656 INTERFACEO |
0 |
|
|
Intersil (Renesas Electronics America) |
EVAL BOARD 1 DFN |
0 |
|
|
Intersil (Renesas Electronics America) |
BOARD EVAL FOR ISL61862 |
0 |
|
|
Intersil (Renesas Electronics America) |
BOARD EVAL FOR ISL61863 |
0 |
|
|
Intersil (Renesas Electronics America) |
EVALUATION PLATFORM ISL6119 |
0 |
|
|
Intersil (Renesas Electronics America) |
EVAL BOARD FOR ISL9519 |
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)