ICs & Semiconductors Archives - Page 189 of 227

Buck-boost converter improves portable device efficiency

Thu, Oct 03, 2019

With a low quiescent current (IQ) in its class of 6-microA and a high peak efficiency of 96 per cent, designers can now maximise a portable device’s battery life with the MAX77827 buck-boost converter from Maxim Integrated Products.

This 1.5A high-efficiency, compact converter allows 1.8V to 5.5V input and 2.3V to 5.3V output, while providing the system stability needed to minimise abrupt or unexpected shutdowns.

During mode transition when VOUT is set to 3.3V with a 15 microsecond rise/fall time, the ripple is less than 1 per cent of the output voltage, says the company. Fast load transient response provides stable system voltage from transient loads pulling down the system voltage. In extreme harsh conditions of going from 0A to 1A load in 15 microseconds, MAX77827 undershoot is controlled to six per cent of the output voltage where the undershoot of the competitive solution is 12 per cent with a longer recovery time.

The MAX77827 addresses the power requirements of low-power wide-area network (LPWAN) applications, asset tracking devices and a variety of Internet of Things applications.

This converter is a suitable solution to support applications with low power requirements because, regardless of the battery voltage variations, it can automatically transition between buck and boost modes to provide a consistent output power supply.

This converter supports space-constrained designs as its WLP measures 2.04mm x 1.64mm and it is less than 15mm² total solution size. It features a single external resistor to set the output voltage to provide additional savings to an external component and board space.

“High adoption of lithium ion batteries in smart consumer electronics is driving the increasing demand for this market, which is projected to reach $106,493 million by 2024,” said Rishab Sharma, analyst for P&S Intelligence. “Complementary technologies that help prolong battery life and stability can only contribute to their continued growth.”

“For any system design where battery life is critical, there is no better solution than the MAX77827,” said Eric Pittana, director for Mobile Power Solutions at Maxim Integrated. “With the lowest quiescent current and highest efficiency, designers can truly optimize their solutions and maximize their system performance. Specifically, for applications such as GPS asset tracking devices with single-use discharge, prolonging battery life is essential.”

http://www.maximintegrated.com

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CrossLinkPlus FPGAs speed and enhance video bridging

Wed, Oct 02, 2019

Lattice Semiconductor has introduced the CrossLinkPlus FPGA family for MIPI D-PHY based embedded vision systems. The new devices are low power FPGAs featuring integrated flash memory, a hardened MIPI D-PHY and high-speed I/Os for instant-on panel display performance, and flexible on-device programming capabilities.

Developers want to enhance the user experience by adding multiple image sensors and/or displays to embedded vision systems, while also meeting system cost and power budgets.

Key features of the CrossLinkPlus family of FPGAs include on-device reprogrammable flash memory to enable instant-on (< 10 ms), hardened, pre-verified MIPI D-PHY interface supporting speeds up to 6 Gbps per port and broad support for high-speed I/O interfaces such as LVDS, SLVS and subLVDS.

Power consumption can be as low as 300 microwatt (standby) or 5 microwatt (operating).

Lattice also provides ready-to-use IPs and reference designs to accelerate implementation of enhanced sensor and display bridging, aggregation, and splitting functionality, a common requirement for industrial, automotive, computing, and consumer applications. There is a comprehensive IP library, including MIPI CSI-2, MIPI DSI, OpenLDI transmitters and receivers. These IPs are compatible with other Lattice FPGAs for easy design portability.

This new series is fully compatible with the Lattice Diamond design software tool flow, from synthesis and design capture through implementation, verification, and programming.

CrossLinkPlus uses its on-chip flash to support instant-on (minimising visual artifacts that detract from the user experience) and flexible device reprogramming in the field.

“The use of MIPI D-PHY in applications ranging from industrial control equipment displays to AI security cameras is booming as OEMs look to capitalize on the economies of scale driven by the MIPI ecosystem,” said Peiju Chiang, product marketing manager, Lattice Semiconductor.

“Lattice’s new CrossLinkPlus FPGAs combine the flexible programmability and speedy parallel processing of FPGAs with vision-specific hardware, software, pre-verified IPs and reference designs. This lets OEMs devote more time to building innovative applications and less time enabling standard functions that don’t offer any competitive differentiation.”

http://www.latticesemi.com

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Adaptable buck-boost converters deliver up to 2.5A in tiny packaging

Tue, Oct 01, 2019

A family of four high-efficiency, low-quiescent-current (IQ) buck-boost converters that feature tiny packaging with minimal external components for a small solution size is now available from Texas Instruments (TI).

The integrated TPS63802, TPS63805, TPS63806 and TPS63810 DC/DC non-inverting buck-boost converters offer wide input and output voltage ranges that scale to support multiple battery-driven applications, helping engineers simplify and accelerate their designs.

Each of the devices automatically selects buck, buck-boost or boost mode according to the operating conditions. Their complete solution size of 19.5 square mm to square mm 25 is a result of compact packaging, an advanced control topology requiring few external multilayer ceramic capacitors, and tiny 0.47-microH inductors.

The devices offer a 1.3-V to 5.5-V input and 1.8-V to 5.2-V output voltage range, to help engineers speed their designs and encourages reuse across multiple applications.

These DC/DC converters are the latest addition to TI’s low-IQ power-management portfolio, providing low 11- to 15-microA IQ for light-load efficiency while minimising power losses and extending run times in battery-driven applications such as portable electronic point-of-sale terminals, grid infrastructure metering devices, wireless sensors and handheld electronic devices.

The TPS63802 is a 2-A buck-boost converter with low 11-microA IQ consumption suitable for pulsed-load applications such as industrial Internet of Things devices. The TPS63805 is a 2-A buck-boost converter with a 22-microF output capacitor and 0.47-microH inductor resulting in a small solution size of 19.5 mm squared that meets the requirements of handheld industrial and personal electronics applications.

The new series also includes the TPS63806, a 2.5-A buck-boost converter with a focus on improved load-step regulation for applications with an aggressive load profile that require tight regulation, such as time-of-flight sensors in smartphones, cameras or augmented reality devices. And the

TPS63810 is a 2.5-A buck-boost converter with I2C interface for dynamic voltage scaling through either a two-wire interface or the VSEL pin, enabling the device to serve as a pre-regulator or voltage envelope tracker for systems found in smartphones, wireless hearing aids or headphones.

http://www.ti.com

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Automotive smart cameras use deep learning

Fri, Sep 27, 2019

Following a collaboration with, StradVision, Renesas Electronics announces the joint development of a deep learning-based object recognition solution for smart cameras. StradVision’s software has been optimised to run on Renesas Electronics’ R-Car SoCs.

The deep learning-based object recognition system is for smart cameras used in next-generation advanced driver assistance system (ADAS) applications and cameras for ADAS Level 2 and above.

Next-generation ADAS implementations require high-precision object recognition capable of detecting vulnerable road users (VRUs) such as pedestrians and cyclists. These systems must also consumer very low power for mass-market mid-tier to entry-level vehicles.

According to Naoki Yoshida, vice president of Automotive Technical Customer Engagement, at Renesas, StradVision is a leader in vision processing technology, with “abundant experience developing ADAS implementations using Renesas’ R-Car SoCs”. The collaboration has produced production-ready solutions “that enable safe and accurate mobility in the future,” said Yoshida. The deep learning based camera system is expected to contribute to the widespread adoption of next-generation ADAS implementations and support the escalating vision sensor requirements expected to arrive in the next few years.

StradVision’s deep learning–based object recognition software delivers high performance in recognising vehicles, pedestrians and lane marking. The high-precision recognition software has been optimised for Renesas R-Car automotive SoCs R-Car V3H and R-Car V3M. These R-Car devices incorporate a dedicated engine for deep learning processing called CNN-IP (Convolution Neural Network Intellectual Property), enabling them to run StradVision’s SVNet automotive deep learning network at high speed with minimal power consumption. The object recognition characteristic realises deep learning–based object recognition while maintaining low power consumption, suitable in mass-produced vehicles, encouraging ADAS adoption.

StradVision’s SVNet deep learning software is an AI perception solution for the mass production of ADAS systems. It is characterised by recognition precision in low-light environments and its ability to deal with occlusion when objects are partially hidden by other objects. The basic software package for the R-Car V3H performs simultaneous vehicles, person and lane recognition, processing the image data at a rate of 25 frames per second. Developers can customise the software, adding signs, markings and other objects as recognition targets. StradVision provides support for deep learning-based object recognition covering all the steps from training through the embedding of software for mass-produced vehicles.

In addition to the CNN-IP dedicated deep learning module, the Renesas R-Car V3H and R-Car V3M feature the IMP-X5 image recognition engine. The on-chip image signal processor (ISP) is designed to convert sensor signals for image rendering and recognition processing. This makes it possible to configure a system using inexpensive cameras without built-in ISPs, reducing the overall bill-of-materials (BoM) cost, says Renesas.

The R-Car SoCs featuring the new joint deep learning solution, including software and development support from StradVision, are scheduled to be available to developers by early 2020.

http://www.renesas.com

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Buck-boost converter improves portable device efficiency

CrossLinkPlus FPGAs speed and enhance video bridging

Adaptable buck-boost converters deliver up to 2.5A in tiny packaging

Automotive smart cameras use deep learning

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