| Image | Part Number | Description / PDF | Quantity | Rfq |
|---|---|---|---|---|
 |
MaxLinear |
EVAL BOARD FOR XR16V794-B 64TQFP |
0 |
|
|
MaxLinear |
EVAL BRD FOR XR16M654D-B 64LQFP |
0 |
|
|
MaxLinear |
EVAL BOARD FOR XR16C854 64TQFP |
0 |
|
|
MaxLinear |
EVAL BOARD FOR V2651 48TQFP |
0 |
|
|
MaxLinear |
EVAL BOARD FOR XR16M780-B 32QFN |
0 |
|
|
MaxLinear |
EVAL BOARD FOR XR16L2550-B 32QFN |
0 |
|
|
MaxLinear |
EVAL BOARD FOR ST16C2550 |
0 |
|
|
MaxLinear |
EVAL BOARD FOR XR19L202 48QFN |
0 |
|
|
MaxLinear |
EVAL BOARD FOR XR16M670-B 24QFN |
0 |
|
|
MaxLinear |
EVAL BOARD FOR ST16C2551 48TQFP |
0 |
|
|
MaxLinear |
EVAL BOARD FOR XR16L651 48TQFP |
0 |
|
|
MaxLinear |
EVAL BOARD FOR M698-B 100QFP |
0 |
|
|
MaxLinear |
EVAL BRD FOR XR16M654-B 68PLCC |
0 |
|
|
MaxLinear |
EVAL BOARD FOR XR16C864 100QFP |
0 |
|
|
MaxLinear |
EVAL BOARD FOR XR16C850 48TQFP |
0 |
|
|
MaxLinear |
EVAL BOARD FOR XR16M580-C 32QFN |
0 |
|
|
MaxLinear |
EVAL BOARD FOR V2750 48TQFP |
0 |
|
|
MaxLinear |
EVAL BOARD FOR XR16M780-A 32QFN |
0 |
|
|
MaxLinear |
EVAL BOARD FOR XR17C152 100TQFP |
0 |
|
|
MaxLinear |
EVAL BOARD FOR XR68M752 48TQFP |
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)