Evaluation and Demonstration Boards and Kits

Image Part Number Description / PDF Quantity Rfq
FSSDC-LQFP144-0.5MM-EVBV1.1

FSSDC-LQFP144-0.5MM-EVBV1.1

Cypress Semiconductor

DEV KIT TOOL KIT PWR MGMT

0

FSSDC-LQFP120-0.5MM-EVBV1.1

FSSDC-LQFP120-0.5MM-EVBV1.1

Cypress Semiconductor

DEV KIT TOOL KIT PWR MGMT

0

MB2198-600

MB2198-600

Cypress Semiconductor

TOOL KIT

0

MB2198-112-E

MB2198-112-E

Cypress Semiconductor

TOOL KIT

0

SK-8FX-TSC-32PMC

SK-8FX-TSC-32PMC

Cypress Semiconductor

DEV KIT TOOL KIT PWR MGMT

0

MB2146-220

MB2146-220

Cypress Semiconductor

TOOL KIT

0

CYP15G0101DX-EVAL

CYP15G0101DX-EVAL

Cypress Semiconductor

EVAL BRD FOR HOTLINK II

0

MB2198-509-E

MB2198-509-E

Cypress Semiconductor

TOOL KIT

0

CY9266-T

CY9266-T

Cypress Semiconductor

EVALUATION BOARD FOR HOTLINK

0

KEL-8816-200-170S

KEL-8816-200-170S

Cypress Semiconductor

DEV KIT TOOL KIT PWR MGMT

0

MB91972EVB-1

MB91972EVB-1

Cypress Semiconductor

TOOL KIT

0

MB2146-223

MB2146-223

Cypress Semiconductor

TOOL KIT

0

MB2198-551

MB2198-551

Cypress Semiconductor

TOOL KIT

0

MB2198-501-E

MB2198-501-E

Cypress Semiconductor

TOOL KIT

0

MB2198-554

MB2198-554

Cypress Semiconductor

TOOL KIT

0

MB2198-505-E

MB2198-505-E

Cypress Semiconductor

TOOL KIT

0

MB2198-600A7-E

MB2198-600A7-E

Cypress Semiconductor

TOOL KIT

0

MB2141A

MB2141A

Cypress Semiconductor

DEV KIT TOOL KIT PWR MGMT

0

MB2147-583-E

MB2147-583-E

Cypress Semiconductor

TOOL KIT

0

MB2147-581

MB2147-581

Cypress Semiconductor

TOOL KIT

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)

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