Evaluation and Demonstration Boards and Kits

Image Part Number Description / PDF Quantity Rfq
EV_ICS-52000-FX

EV_ICS-52000-FX

TDK InvenSense

EVAL BOARD FOR ICS-52000

2

EV_ICS-40300-FX

EV_ICS-40300-FX

TDK InvenSense

ICS-40300 EVALUATION BOARD

59

EV_ICS-40740-FX

EV_ICS-40740-FX

TDK InvenSense

EVAL BOARD FOR THE ICM-40740 ANA

41

EV_T5818-FX

EV_T5818-FX

TDK InvenSense

EVAL BOARD FOR MMICT5818-00-012

27

EV_INMP522-FX

EV_INMP522-FX

TDK InvenSense

EVAL BOARD MEMS MIC INMP522

4

EV_ICS-43432-FX

EV_ICS-43432-FX

TDK InvenSense

ICS-43432 EVALUATION BOARD

26

EV_ICS-40638-FX

EV_ICS-40638-FX

TDK InvenSense

ICS-40638 EVAL BOARD

28

EV_INMP510-FX

EV_INMP510-FX

TDK InvenSense

EVAL BOARD MEMS MIC INMP510

5

EV_ICS-40181-FX

EV_ICS-40181-FX

TDK InvenSense

ICS-40181 EVALUATION BOARD

1

EV_ICS-41350-FX

EV_ICS-41350-FX

TDK InvenSense

ICS-41350 EVALUATION BOARD

48

EV_ICS-40618-FX

EV_ICS-40618-FX

TDK InvenSense

ICS-40618 EVALUATION BOARD

10

EV_ICS-40730-FX

EV_ICS-40730-FX

TDK InvenSense

EVAL BOARD

32

EV_ICS-43434-FX

EV_ICS-43434-FX

TDK InvenSense

ICS-43434 EVALUATION BOARD

42

EV_ICS-40180-FX

EV_ICS-40180-FX

TDK InvenSense

ICS-40180 EVALUATION BOARD

1

EV_INMP521-FX

EV_INMP521-FX

TDK InvenSense

EVAL BOARD MEMS MIC INMP521

0

EV_ICS-40310-FX

EV_ICS-40310-FX

TDK InvenSense

ICS-40310 EVALUATION BOARD

4

EV_INMP521

EV_INMP521

TDK InvenSense

EVAL BOARD MEMS MIC INMP521

0

EV_ICS-52000-ARRAY

EV_ICS-52000-ARRAY

TDK InvenSense

EVAL BOARD FOR ICS-52000

0

EV_INMP441-FX

EV_INMP441-FX

TDK InvenSense

EVAL BOARD MEMS MIC INMP441

0

EV_ICS-40212-FX

EV_ICS-40212-FX

TDK InvenSense

EVAL BOARD

0

Evaluation and Demonstration Boards and Kits

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.

TypeFunctional FeaturesApplication Examples
Microcontroller Development BoardsEmbedded CPUs, GPIOs, integrated peripheralsIoT devices, robotics
FPGA Evaluation BoardsReconfigurable logic, high-speed interfacesCommunication systems, AI accelerators
Sensor Expansion KitsMulti-sensor integration (temperature, motion, etc.)Smart agriculture, environmental monitoring
Wireless Communication ModulesBluetooth/Wi-Fi/LoRa protocols, antenna interfacesConnected 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.

ParameterDescription
Processor Performance (MHz/GHz)Determines computational capability
Memory Capacity (RAM/Flash)Affects program complexity and data storage
Interface TypesDictates 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

ManufacturerRepresentative Products
STMicroelectronicsSTM32 Nucleo Series, SensorTile Kit
IntelIntel Edison, Movidius Neural Compute Stick
XilinxZynq UltraScale+ MPSoC Evaluation Kit
ArduinoArduino 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)

RFQ BOM Call Skype Email
Top