Renesas introduces an ultra-compact sensor module for smart air quality monitoring in homes, schools and public buildings

Renesas Electronics, a global provider of semiconductor solutions, has announced the introduction of the RRH62000, an advanced integrated sensor module for indoor air quality monitoring. As Renesas' first multi-sensor air quality module, this product accurately detects different particle sizes, total volatile organic compounds and gases harmful to human health in a compact design. Its built-in Renesas microcontroller (MCU) provides smart sensor management solutions 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 variety of global air quality standards.

The RRH62000 is one of the smallest sensor modules in its class, measuring just 46.6mm x 34.8mm x 12mm. It has a built-in Renesas RA family MCU and supports 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 (TVOCs), equivalent carbon dioxide values, temperature and humidity in the same system. All key components are pre-integrated and fully calibrated before leaving the factory, allowing developers to start sensor system design out of the box.

Uwe Gunther, Sr. Director, Modules and Solutions Product Line at Renesas said: "Our RRH62000 module represents the next stage in the evolution of sensor fusion technology. The 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 our customers and will continue to drive innovation in sustainable products that reduce environmental impact and improve safety and comfort in life."

Since the outbreak of COVID-19, 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 usually six to ten times more concentrated indoors than outdoors. Such pollutants include dust, paint fumes, cooking fumes, pollen and particles from HVAC filters, 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 than traditional LED methods and is able to monitor the concentration of PM1, PM2.5, PM10 particulate matter - that is, particles with a diameter of 0.3-10μm, as well as absolute or relative TVOC measurements at different power mode Settings, achieving the highest level of accuracy for the measurement of these pollutants. The RRH62000 provides seven sensor outputs simultaneously, and its onboard MCU allows the system to detect ambient air quality data in real time.

The RRH62000 composite module has built-in firmware and artificial intelligence (AI) algorithms that comply with building standards. Engineers can configure sensors to meet the requirements of various green air quality standards in public buildings, including the Healthy Building Standard (WELL), the Residential Ventilation Association (HVI) standard, and the RESET standard. With these features, for example, schools in China can use the same hardware as schools in the United States or elsewhere, simply updating AI-enabled firmware to suit their needs.

Smart sensor devices, such as the Renesas RRH62000 and the recently released RRH46410 gas sensor module, support on-demand controlled ventilation, enabling HVAC systems to adjust air flow based on carbon dioxide levels and human activity to maintain optimal air quality and energy efficiency. Similarly, these modules use AI algorithms to predict when HVAC filters must be replaced or to detect anomalies before a system fails, resulting in significant cost and time savings in system maintenance.