| Image | Part Number | Description / PDF | Quantity | Rfq |
|---|---|---|---|---|
 |
Analog Devices, Inc. |
DEMO BOARD TC2936CGN |
4 |
|
 |
Texas Instruments |
EVAL BOARD FOR LMK61E2-125M00 |
1 |
|
 |
Broadcom |
EVAL BOARD FOR PEX8615 |
0 |
|
 |
Marutsuelec |
TOSHIBA TC78H610FNG EVAL BOARD |
0 |
|
 |
Analog Devices, Inc. |
A2B BUS-POWER SLAVE 4XPDM BD |
0 |
|
 |
Cirrus Logic |
EVAL BOARD FOR CS7250B MEMS MIC |
0 |
|
 |
Texas Instruments |
EVAL BOARD FOR INA260 |
2 |
|
 |
APS |
SCR (THYRISTOR) 3-PHASE CONTROL |
50 |
|
 |
TDK InvenSense |
EVAL BOARD FOR MMICT5818-00-012 |
27 |
|
 |
Texas Instruments |
EVAL BOARD FOR TUSB211 |
1 |
|
 |
APS |
SCR (THYRISTOR) 3-PHASE CONTROL |
50 |
|
 |
APS |
SCR (THYRISTOR) 3-PHASE CONTROL |
50 |
|
 |
STMicroelectronics |
BOARD EVAL FOR VNQ5027AK |
0 |
|
 |
Texas Instruments |
EVAL MODULE FOR DRV8840 |
1 |
|
 |
TRINAMIC Motion Control GmbH |
BREAKOUTBOARD WITH TMC429 |
9 |
|
 |
Texas Instruments |
TLV320AIC3109EVM-K |
2 |
|
 |
Texas Instruments |
TMDSECATCNCD379D |
18 |
|
 |
Silicon Labs |
BOARD EVAL POE PSE SGL PORT |
7 |
|
 |
Zetex Semiconductors (Diodes Inc.) |
EVAL BOARD FOR PI7C9X2G312GPB |
2 |
|
 |
MaxLinear |
GPIO EXPANDER EVAL BOARD |
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)