Renesas Extends RA MCU Family with RA6T1 MCU Group for Motor Control and AI-based Endpoint Predictive Maintenance

TOKYO, Japan ― Renesas Electronics Corporation (TSE:6723), a premier supplier of advanced semiconductor solutions, today announced the extension of its microcontroller (MCU) portfolio designed for motor control applications targeting smart homes, industrial automation, and building automation. Featuring a rich set of peripheral functions and AI-based failure detection, the four new RA6T1 Group MCUs are the latest members of Renesas’ rapidly expanding Arm^®-based RA Family, and the first RA MCUs designed for the unique needs of motor control in home appliances, HVAC, solar inverters, and AC drives.

“As home appliances and building and industrial automation equipment become smarter and more complex, manufacturers are grappling with rising BOM costs to support increasing motor performance demands,” said Roger Wendelken, Senior Vice President of Renesas’ IoT and Infrastructure Business Unit. “The RA6T1 MCUs combine the superior performance and flexibility of the Arm-based RA Family with Renesas’ long-standing motor control expertise. In addition, with the emergence of AI-based needs, Renesas is excited to complement Google’s TensorFlow Lite supported platforms with the RA6T1 motor control and predictive maintenance solution.”

“AI and machine learning are taking predictive maintenance to the next level as the industry advances toward Maintenance 4.0. We are excited to join forces with Renesas and accelerate the adoption of smart home and Industrial IoT applications,” said Ian Nappier, Product Manager at Google. “Integrating our open-source TensorFlow AI framework with Renesas’ powerful RA6T1 MCUs brings breakthrough intelligence to motor control equipment.”

Based on the Arm Cortex^®-M4 core, the new RA6T1 32-bit MCUs operate at 120 MHz and feature a rich collection of peripherals optimized for high performance and precision motor control. The integrated peripheral functions with high-speed analog significantly reduce bill of materials (BOM) cost while boosting motor control performance. For example, a single RA6T1 MCU can simultaneously control up to two brushless DC (BLDC) motors. In addition, the Google TensorFlow™ Lite Micro framework for TinyML applications adds enhanced failure detection to the RA6T1 MCUs, offering customers an intelligent, easy-to-use, and cost-effective sensorless motor system for predictive maintenance. The TensorFlow AI framework detects potentially detrimental anomalies in motor systems earlier and more accurately to help customers improve their predictive maintenance processes and reduce maintenance costs.