Evaluation and Demonstration Boards and Kits

Image Part Number Description / PDF Quantity Rfq
BQ24166EVM-741

BQ24166EVM-741

Texas Instruments

EVAL MODULE FOR BQ24166-741

1

BQ24190EVM-021

BQ24190EVM-021

Texas Instruments

EVALUATION MODULE FOR BQ2419

2

TPD6F002-Q1EVM

TPD6F002-Q1EVM

Texas Instruments

EVALUATION MODULE TPD6F002-Q1

1

TPS259827LEVM

TPS259827LEVM

Texas Instruments

DEVELOPMENT POWER MANAGEMENT

7

TPS65987DJEVM

TPS65987DJEVM

Texas Instruments

TPS65987DJEVM

5

DLPDLCR3010EVM-G2

DLPDLCR3010EVM-G2

Texas Instruments

DLPDLCR3010EVM-G2

11

CDCLVP2104EVM

CDCLVP2104EVM

Texas Instruments

EVAL MODULE FOR CDCLVP2104

1

DP130DSEVM

DP130DSEVM

Texas Instruments

EVAL MOD DUAL-SOURCE FOR DP130

3

AFE5803EVM

AFE5803EVM

Texas Instruments

EVAL MODULE FOR AFE5803

1

LM8335EVM

LM8335EVM

Texas Instruments

MODULE EVAL FOR LM8335

4

TPS3840EVM

TPS3840EVM

Texas Instruments

PWR MGMT SWITCHING REGULATOR

10

TX517EVM

TX517EVM

Texas Instruments

EVAL MODULE FOR TX517

2

BQ2026EVM

BQ2026EVM

Texas Instruments

EVAL MODULE FOR BQ2026

2

TPS24751EVM-546

TPS24751EVM-546

Texas Instruments

EVAL MODULE FOR TPS24751

7

TPS2492EVM-001

TPS2492EVM-001

Texas Instruments

EVAL MODULE FOR TPS2492-001

2

DRV8881PEVM

DRV8881PEVM

Texas Instruments

EVALUATION MODULE DRV8881

2

TPS22976EVM

TPS22976EVM

Texas Instruments

EVALUATION MODULE

6

ISO485EVM

ISO485EVM

Texas Instruments

EVAL MODULE FOR ISO485

1

PSIEVM

PSIEVM

Texas Instruments

EVAL MODULE

2

INA233EVM

INA233EVM

Texas Instruments

EVAL BOARD FOR INA233

17

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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