ST announces mass production for turnkey Bluetooth/Wi-Fi modules developed with Qualcomm

STMicroelectronics has announced mass-production start for its ST67W611M1 combined Wi-Fi 6 and Bluetooth Low Energy 5.4 module, describing the early success of Siana, a lead customer for its fast-to-market connectivity.

The module is the first product of ST’s collaboration with Qualcomm Technologies, announced by the two companies in 2024, to simplify implementing wireless connectivity in systems containing STM32 microcontrollers (MCUs). Their vision, now realised in silicon, fuses ST’s expertise in embedded design and the STM32 ecosystem of microcontrollers, software, and development tools integrated with Qualcomm Technologies’ wireless connectivity technologies.

“Wireless connectivity is a key enabler for the cloud-connected intelligent edge and demand for smart, connected devices continues to expand and accelerate throughout consumer and industrial markets,” said Jerome Vanthournout, Connectivity Business Line Director, STMicroelectronics. “Mastering the complex Wi-Fi and Bluetooth protocols, and bringing that connectivity to devices and IoT applications, are huge challenges. Our modular solution, created with industry-leading knowhow of all aspects, lets product developers focus their resources at the application level and bring new products to market quickly.”

Shishir Gupta, Senior Director, Product Management at Qualcomm Technologies added, “Qualcomm Technologies is thrilled to see the impact of our collaboration with STMicroelectronics through the ST67W module. This module, which contains Qualcomm Technologies’ wireless connectivity components, not only simplifies the integration of Wi-Fi and Bluetooth into a wide range of devices powered by STM32 microcontrollers but also offers incredible flexibility and scalability. This module is a testament to our joint commitment to driving innovation and excellence in the IoT space.”

The ST67W module is ready to integrate with any STM32 MCU and contains a Qualcomm Technologies multiprotocol network coprocessor and 2.4GHz radio. All RF front-end circuitry is built-in, including power/low-noise amplifiers, the RF switch, balun, and integrated PCB antenna, with 4Mbyte Flash for code and data storage and a 40MHz crystal. The module comes pre-loaded with Wi-Fi 6 and Bluetooth 5.4 and is pre-certified according to mandatory specifications. Thread and Matter will be supported soon via software update. There is also an optional coaxial antenna or board-level connections for an external antenna. Security is handled with cryptographic accelerators and services including secure boot and secure debug reaching PSA Certified Level 1, making it easy for customers to comply with the upcoming Cyber Resilience Act and RED directives.

Product developers need no RF design expertise to create a working solution using this module. Highly integrated in a 32-lead LGA package, it is ready to place on the board and permits simple, low-cost PCB designs with as few as two layers.

Siana Systems is among the first IoT technology companies to explore the opportunities this wireless connectivity module brings to enhance product performance and accelerate time to market.

“The ST67W module expands opportunities to add Wi-Fi to devices powered by various STM32 microcontrollers and worry less about the minimum requirements. We can simply integrate the module and quickly get Bluetooth and Wi-Fi connectivity, with minimal additional engineering, which provides us with a simple go-to solution for our next generation designs,” said Sylvain Bernard, Founder and Solution Architect, Siana Systems. “The module’s RF performance, with the radio and front-end circuitry integrated, is very strong, and the flexible power management with fast wake-up times lets us create extremely energy-efficient new products.”

The ST67W611M1 leverages the STM32 ecosystem, which contains over 4,000 commercial part numbers, powerful STM32Cube tools and software, and enhancements that boost edge AI development. The STM32 family covers a broad spectrum from economical Arm® Cortex®-M0+ devices to variants with high-performing cores like Cortex-M55, Cortex-M4 with DSP extensions, and Cortex-A7 in the STM32MP1/2 MPUs.

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Rohm has developed a breakthrough in AI-equipped MCUs

Rohm has developed AI-equipped MCUs (AI MCUs) – ML63Q253x-NNNxx / ML63Q255x-NNNxx – that enable fault prediction and degradation forecasting using sensing data in a wide range of devices, including industrial equipment such as motors. According to Rohm these MCUs are the industry’s first to independently execute both learning and inference without relying on a network connection.

As the need for efficient operation of equipment and machinery continues to grow, early failure detection and enhanced maintenance efficiency have become key challenges. Equipment manufacturers are seeking solutions that allow real-time monitoring of operational status while avoiding the drawbacks of network latency and security risks. Standard AI processing models, however, typically depend on network connectivity and high-performance CPUs, which can be costly and difficult to install.

In response, Rohm has developed AI MCUs that enable standalone AI learning and inference directly on the device. These network-independent solutions support early anomaly detection before equipment failure – contributing to a more stable, efficient system operation by reducing maintenance costs and the risk of line stoppages.

The new products adopt a simple 3-layer neural network algorithm to implement Rohm’s proprietary on-device AI solution “Solist-AI™.” This enables the MCUs to perform learning and inference independently, without the need for cloud or network connectivity.

AI processing models are generally classified into three types: cloud-based, edge, and endpoint AI. Cloud-based AI performs both training and inference in the cloud, while edge AI utilises a combination of cloud and on-site systems - such as factory equipment and PLCs - connected via a network. Typical endpoint AI conducts training in the cloud and performs inference on local devices, so network connection is still required. Furthermore, these models typically perform inference via software, necessitating the use of GPUs or high-performance CPUs.

In contrast, Rohm’s AI MCUs, although categorised as endpoint AI, can independently carry out both learning and inference through on-device learning, allowing for flexible adaptation to different installation environments and unit-to-unit variations, even within the same equipment model. Equipped with Rohm’s proprietary AI accelerator “AxlCORE-ODL,” these MCUs deliver approximately 1,000 times faster AI processing compared to Rohm’s conventional software-based MCUs (theoretical value at 12MHz operation), enabling real-time detection and numerical output of anomalies that “deviate from the norm”. In addition, high-speed learning (on-site) at the point of installation is possible, making them ideal for retrofitting into existing equipment.

These AI MCUs feature a 32-bit Arm® Cortex-M0+ core, CAN FD controller, 3-phase motor control PWM, and dual A/D converters, achieving a low power consumption of approximately 40mW. As such, they are ideally suited for fault prediction and anomaly detection in industrial equipment, residential facilities, and home appliances.

The lineup will consist of 16 products in different memory sizes, package types, pin counts, and packaging specifications. Mass production of 8 models in the TQFP package began sequentially in February 2025. Among these, two models with 256KB of Code Flash memory and taping packaging are available for purchase, along with an MCU evaluation board, through online distributors.

Rohm has released an AI simulation tool (Solist-AI Sim) on its website that allows users to evaluate the effectiveness of learning and inference prior to deploying the AI MCU. The data generated by this tool can also serve as training data for the actual AI MCU, supporting pre-implementation validation and improving inference accuracy.

 

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Integrated matching filters from ST now available for long-range wireless MCUs

STMicroelectronics has released a set of antenna-matching companion chips for STM32WL33 wireless microcontrollers (MCUs) to simplify the development of IoT, smart-metering, and remote-monitoring applications.

The new MLPF-WL-01D3/02D3/04D3 ICs combine impedance matching and harmonic filtering on a single glass substrate to optimise the main radio’s RF performance, leveraging proprietary integrated passive device (IPD) technology. Precision engineered and housed in an ultra-compact package, the highly integrated devices ensure right-first-time design while saving development time, bill-of-materials costs, and board space.

Integrating antenna protection, the new devices substantially simplify the RF connection to the MCU. In addition to ensuring optimal performance, integrated matching and filtering also increases reliability and reduces overall cost by eliminating the need to combine multiple discrete components.

In total, there will be seven variants, letting designers optimise for radio operation in high-band (826-958MHz) or low-band (413-479MHz), high-power (16dBm/20dBm) or low-power (10dBm), and for 4-layer or 2-layer PCB designs. The non-conductive glass substrate ensures outstanding performance with minimal temperature drift, and the chip-scale package has extremely compact dimensions, measuring just 1.47mm x 1.87mm and 630µm high after reflow.

The long-range sub-GHz radio of ST’s STM32WL33 wireless MCUs can operate in either of the 413-479MHz and 826-958MHz license-free bands and with up to 20dBm output power where local regulations permit. With an Arm® Cortex®-M0+ core and selected peripherals also on-chip, the highly integrated wireless MCUs simplify the design of remote-monitoring equipment for smart city, smart agriculture, and smart industry. Applications include smart meters, and safety systems, asset-trackers, and proximity detection. A set of reference designs (STDES-WL3xxxx), pre-certified and fine-tuned for STM32WL33 MCUs, is also available.

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Rohm develops new compact surface-mount near-infrared LEDs

Rohm has expanded its portfolio of surface-mount near-infrared (NIR) LEDs with new compact top-view types. They are optimised for applications such as VR/AR devices, industrial optical sensors, and human detection sensors.

The demand for advanced sensing technologies utilising near-infrared (NIR) has grown in recent years, particularly in VR/AR equipment and biosensing devices. These technologies are used in applications such as eye tracking, iris recognition, and blood flow/oxygen saturation measurements that require high accuracy. At the same time, miniaturisation, energy efficiency, and design flexibility are becoming increasingly important. In industrial equipment, near-infrared LEDs are playing a greater role with the rise of precise printer control and automation systems. In response, Rohm is expanding customer options by developing a lineup of compact packages and wavelengths that offer greater design flexibility, while contributing to higher precision and power savings by achieving high radiant intensity.

The new lineup consists of six models in three package configurations, including two ultra-compact (1.0mm × 0.6mm), ultra-thin (t=0.2mm) products as part of the PICOLED series: SML-P14RW and SML-P14R3W. In addition, there are four variants in the industry-standard (1.6mm × 0.8mm) size, featuring a narrow beam circular lens package (CSL0902RT, CSL0902R3T) and flat lens design that emits light over a wide range (CSL1002RT, CSL1002R3T). Each package is available in two wavelengths, 850nm (860nm for the SML-P14RW) and 940nm, allowing customers various options for their specific application needs. The 850nm wavelength is ideal for phototransistors and camera sensors, making it suitable for high-sensitivity applications such as eye tracking and object detection in VR/AR. At the same time, the 940nm wavelength is less affected by sunlight and does not appear red when emitting light, making it suitable for motion sensors. It is also widely used in biosensing applications such as pulse oximeters to measure blood flow and oxygen saturation (SpO2).

The light source incorporates an NIR element with an optimised emission layer structure utilising proprietary technology developed through in-house manufacturing expertise. This has made it possible to achieve industry-leading radiant intensity in a compact package, which was previously considered difficult. For example, compared to a standard 1006 size product, the SML-P14RW delivers approx. 1.4 times the radiant intensity at the same current. In other words, the SML-P14RW consumes 30% less power to achieve the same radiation intensity. This technology improves sensing accuracy and power savings for the entire system.

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