BEIJING, China, Aug. 21, 2024 - Renesas Electronics Corporation (TSE: 6723), a global provider of semiconductor solutions, today announced the launch of the RRH62000, an advanced all-in-one sensor module for indoor air quality monitoring, which accurately detects particulate matter of different sizes, total volatile organic compounds (TOCs) and gases harmful to human health, all in a compact design. and gases that are harmful to human health in a compact design. Its built-in Renesas microcontroller (MCU) provides an intelligent sensor management solution for the growing market of air monitoring applications such as air purifiers, smoke detectors, heating, ventilation and air conditioning (HVAC) systems, weather stations and smart home systems. In addition, its robust firmware enables customers to adapt their products to a wide range of global air quality standards.

The RRH62000 is one of the smallest sensor modules of its kind, measuring just 46.6mm x 34.8mm x 12mm, with a built-in Renesas RA product family MCU and support for seven sensor signals: the laser-based PM1/2.5/10 sensor, the ZMOD4410 gas sensor, and the HS4003 humidity and temperature sensor. These sensors detect particulate matter, total volatile organic compounds (TVOC), equivalent CO2 values, temperature and humidity in the same system. All key components are pre-integrated and fully calibrated at the factory, allowing developers to start sensor system design right out of the box.

Our RRH62000 module represents the next stage in the evolution of sensor fusion technology,’ said Uwe Gunther, Sr. Director, Modules and Solutions Product Line at Renesas. This technology fuses data from multiple sensors and turns it into comprehensive and practical insights at the environmental monitoring level. Renesas is committed to creating integrated sensing solutions that simplify the development process for its customers and will continue to drive innovation in sustainable products that reduce environmental impact and increase safety and comfort in life.’

Since the outbreak of the New Crown epidemic, public concern about air quality and its impact on health has increased significantly. Today, people are increasingly aware of the dangers of air pollutants on the respiratory system and overall health. What is less well known is that pollutants are typically six to ten times more concentrated indoors than outdoors. Such pollutants include dust, paint odours, cooking fumes, pollen and particles from HVAC filters, among others, which can enter the respiratory system and cause lung damage, cancer and other health problems.

To address these new challenges, Renesas' new sensor module monitors a wide range of air quality conditions. It uses laser technology with higher accuracy compared to traditional LED methods to monitor the concentration of PM1, PM2.5, and PM10 particles - that is, particles with diameters between 0.3 and 10 μm - as well as absolute or relative TVOC measurements at different power mode settings, providing the highest level of accuracy for the measurement of these pollutants. The RRH62000 provides seven sensor outputs simultaneously and its on-board MCU allows the system to detect ambient air quality data in real time.

The RRH62000 combo module has inbuilt building standard compliant firmware and artificial intelligence (AI) algorithms. Engineers can configure the sensors to meet the requirements of various green air quality standards in public buildings, including the ‘Healthy Buildings Standard (WELL),’ the ‘Home Ventilation Institute (HVI)’ standard, and the ‘RESET RESET’ standards. For example, with these features, schools in China can use the same hardware as schools in the U.S. or elsewhere and simply update the AI-enabled firmware to suit their needs.

Smart sensor devices, such as the Renesas RRH62000 and the recently announced RRH46410 gas sensor modules, support on-demand control of ventilation, enabling HVAC systems to adjust air flow based on CO2 levels and occupant activity to maintain optimal air quality and energy efficiency. Similarly, these modules use AI algorithms to predict when HVAC filters must be changed or to detect anomalies before a system failure occurs, resulting in significant cost and time savings in system maintenance.