
An integrated 1000Base-T1 PHY transceiver and design resources combine to accelerate the design of real-time single-pair Ethernet systems.

Use a development board and associated software to speed the deployment of secure, multiprotocol IoT applications.

A multicore processor, development board, and software combine to speed the design of edge AI applications.

A broad portfolio of AEC-Q200 parts meets diverse requirements for high performance, reliability, and safety in advanced automotive electronic subsystems.

Use ultra-low-power MCUs with specialized capabilities to meet application requirements without compromising processor performance or power budgets.

Use a compact, low-profile 5G MIMO antenna for effective connectivity while maintaining application discreetness and aesthetics.

Using a suite of highly integrated MCUs, developers can optimize their product designs for power, performance, security, and wireless connectivity.

Developers can use precision data acquisition modules and highly integrated IMUs to implement sophisticated and cost-effective avionics systems.

A set of devices simplifies the design of Ethernet connections capable of resisting surges associated with long cables between field sensors and hosts.

A pair of devices simplify the design of wireless charging required in a growing class of space-constrained and sealed devices.

A fully integrated RF transceiver effectively meets the demand for high-performance agile SDR systems with reduced SWaP.

A comprehensive set of devices addresses requirements for sensor interfaces, processors, power, and connectivity in intelligent field instruments.

Use bonded-pair Ethernet cables instead of twisted-pair cables to more effectively meet the electrical and physical challenges of industrial environments.

Use a pre-certified LoRaWAN module to quickly deploy secure long-range connectivity for end devices.

Use a combination of security ICs with high-performance DSCs and low-power MCUs to implement an effective embedded systems security platform.

The implementation of machine learning at the edge has been greatly simplified by NXP’s i.MX RT1170 Crossover MCU and associated board and software tools.
Hardware and software support for edge-based machine learning applications.

Built around an integrated ToF sensor, an evaluation kit and associated software simplify development of 1D and 3D industrial sensing applications.
Vision-centric hardware and software simplify development of adaptable smart vision applications.
Using a platform of specialized devices and boards, designers can quickly and efficiently develop and implement highly flexible EV charging stations.