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Released the solution allows anyone to easily learn and develop security functions using RL78/F24.

RL78/F24で誰でも簡単にセキュリティ機能を学び、開発できるソリューションをリリース!

RL78/F24 セキュリティRSSKを使えば難しい鍵登録データやSecure Boot のMACもGUIで簡単生成!
安価なセキュリティ初心者向けツールとしても活用可能!

Best-in-Class Performance Analog Front End Embedded in RX23E-B MCU for Industrial Sensor Applications

産業用センサ機器向け最高クラスのアナログフロントエンドMCU「RX23E-B」

ディスクリート製品に匹敵する125kSPSのΔΣADCを搭載したRX23E-Bが加わり、産業用センサ機器の大半をサポートできるようになりました。

Introduction of Noise Countermeasure Guide for Capacitive Touch Sensors of Renesas MCU

静電容量式タッチセンサのノイズ対策ガイドのご紹介

国際規格 IEC61000-4 に定める各イミュニティノイズに対して、静電容量式タッチセンサを使用したシステムのノイズ耐性を向上するガイドをご紹介します。

 Success Story with Renesas: NexCOBOT Co. Ltd.

Success Story with Renesas: NexCOBOT Co. Ltd.

Renesas FuSa solution helps customers. We’ve been involved in safety application projects globally and NexCOBOT Co., Ltd. has successfully implemented their latest safety. Here is the story.

Energy-Efficient Motor Control for Home Appliances Blog

Energy-Efficient Motor Control for Home Appliances

This blog presents a market overview of the motors used in consumer appliances, energy optimization challenges, and the Renesas MCU infrastructure supporting motor control applications in consumer appliances.

NFC Revolutionizing IoT Applications: A Seamless Future Blog

NFC Revolutionizing IoT Applications: A Seamless Future

Embrace the future of connectivity and enhance your application with Renesas' PTX105R NFC controller for Internet of Things applications, the most suitable NFC reader on the market.

Renesas QEMU Environment is a simulator for R-Car S4 that achieves high-speed simulation

高速なシミュレーションを実現するR-Car S4用シミュレータRenesas QEMU Environment

高速なシミュレーションを実現するR-Car S4用シミュレータRenesas QEMU Environmentをご紹介します。

AI Coding with ChatGPT for RL78 Microcontrollers (Arduino)

Chat-GPTを利用したRL78マイコンのAIコーディング (Arduino)

このブログでは、Chat-GPTとRL78 Arduino互換ボードを使用してAIコーディングを行う方法と、その実例を紹介します。

 The IoT Cloud Connection Decision: Wi-Fi or Cellular?

The IoT Cloud Connection Decision: Wi-Fi or Cellular?

With the ubiquity of wireless connectivity options on IoT devices, connection to the cloud has become more flexible than ever. Traditionally, Wi-Fi has been the most popular choice in the IoT world although this has changed in recent years with the huge strides made in cellular networks. However, there is no right answer for which option would be the best or the more efficient between the two.

The type of network connection to use depends on several factors that affect solution development.

Power Your Edge AI Application with the Industry’s Most Powerful Arm MCUs

Power Your Edge AI Application with the Industry’s Most Powerful Arm MCUs

The AIoT has been enabled by higher MCU compute capability and thin neural network models that are more suited for edge and end point devices than traditional CPUs and MPUs.