Evaluation and Demonstration Boards and Kits

Image Part Number Description / PDF Quantity Rfq
BQ25606EVM-772

BQ25606EVM-772

Texas Instruments

EVAL BOARD FOR BQ25606

2

DRV8662EVM

DRV8662EVM

Texas Instruments

EVAL MODULE FOR DRV8662

13

TPD3S714-Q1EVM

TPD3S714-Q1EVM

Texas Instruments

EVALUATION MODULE

5

LM3658SD-AEV/NOPB

LM3658SD-AEV/NOPB

Texas Instruments

KIT EVAL FOR LM3658SD

4

DS16EV51-AEVKH

DS16EV51-AEVKH

Texas Instruments

BOARD EVAL FOR DS16EV5110A

2

TPS23756EVM

TPS23756EVM

Texas Instruments

EVAL MODULE FOR TPS23756

3

TPS22968NEVM

TPS22968NEVM

Texas Instruments

EVALUATION MODULE

10

BQ24035EVM

BQ24035EVM

Texas Instruments

EVALUATION MODULE FOR BQ24035

5

BQ77915EVM-014

BQ77915EVM-014

Texas Instruments

DEVELOPMENT POWER MANAGEMENT

0

BQ24078EVM-015

BQ24078EVM-015

Texas Instruments

EVAL BOARD FOR BQ24078

1

REF6025EVM-PDK

REF6025EVM-PDK

Texas Instruments

EVAL BOARD FOR REF6025

5

LMK61A2-312M50EVM

LMK61A2-312M50EVM

Texas Instruments

EVALUATION MODULE

4

DP130SSEVM

DP130SSEVM

Texas Instruments

EVAL MOD SINGLE-SOURCE FOR DP130

2

EVM430-F67641

EVM430-F67641

Texas Instruments

EVAL MODULE FOR F67641

2

UCC5870QDWJEVM-026

UCC5870QDWJEVM-026

Texas Instruments

UCC5870-Q1 FUNCTIONAL SAFETY COM

9

DLP4710EVM-LC

DLP4710EVM-LC

Texas Instruments

DLP PROCESSOR

3

SM3320-BATT-EV/NOPB

SM3320-BATT-EV/NOPB

Texas Instruments

EVAL CHARGE CONTROL BATTERY

3

TPD3S014-Q1EVM

TPD3S014-Q1EVM

Texas Instruments

EVALUATION MODULE

1

BQ24261EVM-079

BQ24261EVM-079

Texas Instruments

EVAL MOD LI-ION BATTERY CHARGER

3

BOOSTXL-DRV8304H

BOOSTXL-DRV8304H

Texas Instruments

BOOSTXL-DRV8304H

2

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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