| Image | Part Number | Description / PDF | Quantity | Rfq |
|---|---|---|---|---|
 |
Renesas Electronics America |
VERSACLOCK 3S 5P35021 DEV KIT |
1 |
|
 |
Renesas Electronics America |
EVAL VERSACLOCK 5 PROGRAM CLK |
2 |
|
 |
Renesas Electronics America |
EVAL BOARD 5P49V5908 VERSACLOCK5 |
0 |
|
 |
Renesas Electronics America |
VERSACLOCK 3S 5L35021 DEV KIT |
13 |
|
 |
Renesas Electronics America |
RX23T 24V MOTOR CONTROL EVAL BRD |
2 |
|
 |
Renesas Electronics America |
EVAL BOARD FOR 5P49V6965 |
14 |
|
 |
Renesas Electronics America |
15 WATT WIRELESS POWER TRANSMITT |
0 |
|
 |
Renesas Electronics America |
MICROCLOCK 5X2503 DEVELOPMENT KI |
3 |
|
 |
Renesas Electronics America |
EVALUATION KIT |
0 |
|
 |
Renesas Electronics America |
MOTOR CONTROLS KIT FOR RX62T |
1 |
|
 |
Renesas Electronics America |
EVAL BOARD FOR 82P33831-1 |
2 |
|
 |
Renesas Electronics America |
EVAL VERSACLOCK 5 PROGRAM CLK |
0 |
|
 |
Renesas Electronics America |
EVAL BOARD FOR 82P33814 |
0 |
|
 |
Renesas Electronics America |
BOARD |
0 |
|
|
Renesas Electronics America |
SSC BOARD ZSC31015 V1.1 WITH SAM |
0 |
|
|
Renesas Electronics America |
ZSPM1063-KIT01 EVALUATION KIT V1 |
0 |
|
|
Renesas Electronics America |
ZSSC313X MASS CALIBRATION SYSTEM |
0 |
|
|
Renesas Electronics America |
SSC BOARD ZSC31014 V2.1 WITH SAM |
0 |
|
|
Renesas Electronics America |
ZSPM4141W12KIT EVALUATION KIT V1 |
0 |
|
|
Renesas Electronics America |
NETWORK TIMING |
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)