Weartech-ICs & Semiconductors Archives - Page 24 of 41

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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Ambiq increases security in power processor SoCs

Wed, Mar 16, 2022

Additions to the Apollo4 SoC family by Ambiq are the Apollo4 Plus and Apollo4 Blue Plus with Bluetooth Low Energy connectivity. They have robust security features, said the company, to better protect power-constrained IoT endpoint devices without compromising power efficiency.

The Apollo4 Plus is the fourth generation system processor built upon Ambiq’s proprietary Subthreshold Power-Optimized Technology (SPOT), enabling new features while reducing devices’ overall system power consumption to extend their battery life. Embedded with Mbytes of MRAM, SRAM, low power processors, solid software stacks and up to 192MHz operating frequency with TurboSPOT, the Apollo4 Plus enables more AI-capable operations, including data ingestion, pre-processing, inference and actuation. Apollo4 has a low power, end-to-end audio subsystem, to run compute complex algorithms needed for precise voice recognition and higher fidelity voice capability needed for voice calls. Its integrated GPU and display controller, coupled with fast and efficient memory access, offer manufacturers the ability to differentiate products with bigger and richer display user interfaces with vivid colours, high-resolution and smooth graphics. Ambiq’s Secure by Design features allow OEMs to secure products from the ground up when implementing SecureSPOT with tools to implement end-to-end security from the start of the design.

“The future of IoT is in the intelligence of things that stay on and connected 24/7,” said Dan Cermak, vice president of Architecture and Product Planning at Ambiq. “The latest product and feature additions to our Apollo4 SoC family demonstrate that battery-operated devices no longer have to compromise performance for power constraints.”

Apollo4 Plus is now in mass production. The enhanced graphics display and greater voice capabilities serve as either an application processor or a coprocessor for battery-powered endpoint devices, said Ambiq. Target applications are smartwatches and smart bands, consumer medical devices, motion and tracking units and smart home devices.

Ambiq specialises in energy-efficient semiconductors for battery-powered IoT endpoint devices. Ambiq has helped leading manufacturers worldwide develop products that can operate for days, months, and sometimes years, on a battery, and even do away with the battery entirely by harvesting energy.

Ambiq’s patented Subthreshold Power Optimized Technology (SPOT) platform has enhanced IoT endpoint devices by enabling a significant increase in compute power at reduced energy levels. The company says its goal is to bring artificial intelligence (AI) where it has never gone before in mobile and portable devices using Ambiq’s low power microcontrollers and SoCs.

http://www.ambiq.com

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MEMS sensors add machine learning in the edge and electrostatic sensing

Tue, Mar 01, 2022

A third generation of MEMS sensors are intended for consumer mobiles and smart industries, healthcare, and retail.

MEMS technology drives the intuitive context-aware features of today’s smartphones and wearables. STMicroelectronics claims that its latest generation of MEMS sensors take performance beyond established technical limitations on output accuracy and power consumption. The new sensors can deliver the highest accuracy for product features such as activity detection, indoor navigation, and precision industrial sensing. At the same time, they keep battery demand low for longer runtimes.

Selected variants include extra features such as ST’s machine learning core (MLC) and electrostatic sensing. The MLC brings adaptive, machine learning capabilities to edge applications that operate at extremely low power. The charge variation (QVAR) sensing channel monitors changes in electrostatic charge, either through contact with the body in a smartwatch or fitness band or by non-contact sensing (radar). The MEMS sensors with QVAR can enhance user interface controls for seamless interactions or to simplify detection of moisture and condensation. Radar mode applications include human presence detection, activity monitoring and people counting.

The LPS22DF and waterproof LPS28DFW barometric pressure sensors operate from 1.7 microA and have absolute pressure accuracy of 0.5hPa. The LPS28DFW has dual full scale capability, enabling accurate vertical position both underwater and out of water. The full scale range is selectable up to 1260 or 4060hPa, equivalent to water pressure at a depth of about 98 feet (30m). The sensors enhance altimeter and barometer performance in portable devices and wearables including sport watches. Typical industrial applications include equipment for weather monitoring and accurate water-depth sensing.

The LIS2DU12 three-axis accelerometer has been designed to build a low power architecture with active anti-aliasing. The anti-aliasing filter operates with a current consumption among the lowest in the market, claims STMicroelectronics. The LIS2DU12 consumes only 3.5 microA at 100Hz output data rate (ODR) and is also the first accelerometer with I3C interface, according to the company.

The accelerometer is a small footprint of 2.0 x 2.0 x 0.74mm. The accelerometer is designed for wearable devices, hearing aids, true wireless stereo (TWS) and wireless sensor nodes.

The LSM6DSV16X six-axis iNEMO inertial module contains QVAR electrostatic sensing as well as the MLC and a finite state machine (FSM). The operating current can be as little as 12 microA. The FSM enables adaptive self-configuration (ASC). With ASC, the device understands the context and reconfigures itself without waking up the system, gaining a significant extra power saving.

The MEMS sensor will enter production and has been demonstrated in an electrostatic radar application for early user detection to accelerate waking-up a laptop PC.

The LPS22DF pressure sensor is supplied in a 2.0 x 2.0 x 0.73mm 10-lead LGA package and the LPS28DFW is supplied in a 2.8 x 2.8 x 1.95mm seven-lead LGA are in production now. Free samples are available. The LIS2DU12 and LSM6DSV16X are scheduled for production later in 2022.

http://www.st.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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Nowi unveil energy harvesting PMIC with a cold start feature

Ambiq increases security in power processor SoCs

MEMS sensors add machine learning in the edge and electrostatic sensing

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

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