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Barometric pressure sensor tracks at altitude

Wed, Apr 06, 2022

Precision and low power consumption are combined in the BMP581, said Bosch Sensortec. It is the company’s first capacitive barometric pressure sensor and the company claimed it raises the bar for accuracy and performance in mobile devices.

The BMP581 combines highest precision and low power consumption to enable fitness tracking, fall detection and indoor localisation in wearables, hearables or IoT devices.

Commenting on accuracy of the BMP581, Bosch Sensortec’s CEO, Dr Stefan Finkbeiner, said: “It’s breathtakingly accurate: it can measure a barometric pressure fluctuation that’s equivalent to one-thousandth of the weight of a mosquito (7.6 μg).”

This level of accuracy enables the sensor to detect an altitude change of just a few centimetres. It can, therefore, monitor movement in fitness applications down to the level of individual pull-ups or push-ups, and can provide highly accurate location information for indoor localisation, navigation, and floor detection to provide key data for emergency call requirements (E-911).

The sensor can noticeably improve flight stability and landing accuracy in drones, and help detect water levels in household appliances to avoid flooding, added Bosch Sensortec.

It provides relative accuracy of +/-0.06 hPa and a typical absolute accuracy of +/-0.3 hPa. Full accuracy is available over a wide measurement range from 300 to 1100 hPa. The BMP581 has a typical temperature co-efficient offset (TCO) of just +/-0.5 Pa/K and low RMS noise of 0.08 Pa at 1000 hPa (typical). Long-term drift over 12 months is only +/-0.1 hPa.

Compared to the company’s previous generation of barometric pressure sensors, the BMP390, the BMP581 draws 85 per cent less current, noise is 80 per cent lower, and TCO is reduced by 33 per cent.

Typical current consumption of just 1.3 microA at 1Hz “substantially extends battery life”, said Bosch Sensortec, while in deep standby mode, the sensor draws only 0.5 microA. The sensor provides an I2C, I3C and SPI (three-wire / four-wire) digital, serial interface.

The BMP581 is provided in a compact 10-pin LGA package shielded by a metal cover, measuring just 2.0 x 2.0 x 0.75mm³.

The BMP581 is available now.

https://www.bosch-sensortec.com

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Nowi unveil energy harvesting PMIC with a cold start feature

Fri, Apr 01, 2022

Dutch semiconductor company, Nowi, extends its energy harvesting and power management portfolio with the Diatom chipset. The 4.0 x 4.0mm Diatom (NH16D3045) is an energy harvesting PMIC, which has a wide power input range from micro W to mW and a fast MPPT (maximum power point tracking) for efficient energy harvesting.

It is designed to extract the power output of a wide range of energy harvesters to charge a variety of energy storage elements such as rechargeable batteries or supercapacitors.

The cold start feature enables batteryless applications, which helps companies reduce maintenance costs, as well as a more sustainable and easier to use option, the company said.

Diatom caters to the need for increased integration in order to lower implementation cost, size and complexity whilst improving performance, added Nowi. It combines integrated energy harvesting and power management into a single product and has regulated output, over-voltage protection and USB charging.

Diatom enables power autonomy in a variety of low power applications, from the smart home to industry 4.0 and retail applications. It can be used in IoT devices, electronic shelf labels (ESLs), to smart wearables such as smart bands, glasses, and consumer electronics like remote controls, tags.

According to Nowi, Diatom perpetually powers devices with clean ambient energy, simplifies the design process and lowers the threshold to develop energy autonomous products.

Simon van der Jagt, CEO at Nowi, said that the inductorless design and integrated power management functionalities will contribute to reduced implementation cost and area required, and make new designs possible.

Semiconductor company, Nowi was founded in 2016, based in Delft, the Netherlands. It ha regional offices in the US and in Shanghai.

Nowi has developed energy harvesting power management ICs (PMICs) that combine harvesting performance with small assembly footprint and low bill of materials (BoM) cost.

http://www.nowi-energy.com

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SoC uses computing-in-memory for speech processing at the edge

Tue, Mar 01, 2022

Computing-in-memory technology is poised to eliminate the massive data communications bottlenecks associated with AI speech processing at the network’s edge, said Witinmen. The company has worked with Microchip Technology’s subsidiary Silicon Storage Technology (SST) to develop an embedded memory that simultaneously performs neural network computation and stores weights. Microchip Technology announced that its SuperFlash memBrain neuromorphic memory has been combined with the Witinmem neural processing SoC. The SoC is claimed to be the first in volume production that enables sub-mA systems to reduce speech noise and recognise hundreds of command words, in real time and immediately after power-up.

Microchip has worked with Witinmem to incorporate Microchip’s memBrain analogue in-memory computing, based on SuperFlash technology, into Witinmem’s low-power SoC. The SoC features computing-in-memory technology for neural networks processing including speech recognition, voice-print recognition, deep speech noise reduction, scene detection, and health status monitoring. Witinmem is working with multiple customers to bring products to market during 2022 based on this SoC.

“Witinmem is breaking new ground with Microchip’s memBrain solution for addressing the compute-intensive requirements of real time AI speech at the network edge based on advanced neural network models,” said Shaodi Wang, CEO of Witinmem. “We were the first to develop a computing-in-memory chip for audio in 2019, and now we have achieved another milestone with volume production of this technology in our ultra-low-power neural processing SoC that streamlines and improves speech processing performance in intelligent voice and health products.”

Microchip’s memBrain neuromorphic memory is optimised to perform vector matrix multiplication (VMM) for neural networks. It enables processors used in battery-powered and deeply-embedded edge devices to deliver the highest possible AI inference performance per Watt. This is accomplished by both storing the neural model weights as values in the memory array and using the memory array as the neural compute element. The result is 10 to 20 times lower power consumption than alternative approaches, claims Microchip, and a lower overall processor bill of materials (BoM) costs because external DRAM and NOR are not required.

Permanently storing neural models inside the memBrain’s processing element also supports instant-on functionality for real time neural network processing. Witinmem has leveraged SuperFlash technology’s floating gate cells’ non-volatility to power down its computing-in-memory macros during the idle state to further reduce leakage power in demanding IoT use cases.

http://www.sst.com

http://www.microchip.com

http://www.witintech.com

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STMicroelectronics integrates AI to MEMS sensors

Fri, Feb 18, 2022

Signal processing and AI algorithms have been combined in MEMS (micro electromechanical systems) sensors by STMicroelectronics. The Intelligent Sensor Processing Unit (ISPU) injects local decision-making while substantially saving space and power, says the company.

The ISPU combines a digital signal processor (DSP) able to run AI algorithms and a MEMS sensor on the same silicon. In addition to a reduction in size, compared to system-in-package devices, the ISPU is also claimed to cut power by up to 80 per cent. Merging sensor and AI puts electronic decision-making at the edge, said ST, where products enabled by smart sensors are able to sense, process, and take actions, in what has been called the Onlife Era, fusing technology and the physical world.

The Onlife Era acknowledges living with continuous assistance from connected technologies, enjoying natural, transparent interactions, and seamless transitions, with no discernible distinction between online and offline, ST explained. The ISPU allows the migration of intelligent processing into sensors that support the fabric of life, or as ST puts it: no longer at the edge but in the edge.

The proprietary low power DSP can be programmed in C, a language familiar to many engineers. It also allows quantised AI sensors to support full- to single-bit-precision neural networks. This ensures superior accuracy and efficiency in tasks such as activity recognition and anomaly detection by analysing inertial data, said ST.

“While technically challenging, integrating ST’s sensors on the same piece of silicon with our ISPU does improve sensor-based systems from an online experience to an Onlife one,” said Andrea Onetti, executive vice president, MEMS Sub-Group, at STMicroelectronics.

“It advances the sensor’s features to speed decision-making by reducing data transfers, enhancing privacy by keeping data local, while reducing size and power consumption, which cuts costs,” he added.

“Moreover, the ISPU is easily programmable with commercial AI models and can ultimately operate with all of the leading AI tools.”

ST’s proprietary, C-language-programmable DSP is an enhanced 32-bit RISC (reduced instruction set computing) machine. It is extensible (in the chip-design phase) for dedicated instructions and hardware components. The processor offers a full precision floating point unit, uses a fast four-stage pipeline, operates from 16-bit variable-length instructions, and includes a single-cycle 16-bit multiplier. Interrupt response is four cycles.

ST’s sensors with ISPUs will be packaged in standard 3.0 x 2.5 x 0.83mm packages and will be pin compatible with earlier models available from the company, for ease of upgrades.

ST also claims that combining the sensor and ISPU save five to six time power saving compared with system-in-package approaches in sensor-fusion applications. They also show a two to three times saving in Run mode.

http://www.st.com

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Barometric pressure sensor tracks at altitude

Nowi unveil energy harvesting PMIC with a cold start feature

SoC uses computing-in-memory for speech processing at the edge

STMicroelectronics integrates AI to MEMS sensors

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