Sector News

Mouser boosts high-speed wired connectivity; ships MaxLinear’s G.hn Wave-2 platform

Thu, Jan 24, 2019

Distributor Mouser Electronics stocks the G.hn Wave-2 networking chipset from MaxLinear, featuring the G.hn digital baseband and G.hn analogue front ends. Offering physical data rates up to 2Gbits per second across a variety of physical media, the Wave-2 networking G.hn processors deliver reliable connectivity and reduced congestion in smart grid, security, broadband, industrial, smart home, and automotive applications.

MaxLinear’s G.hn Wave-2 networking platform offers designers the flexibility to combine footprint-compatible components to address multiple G.hn applications. The MaxLinear G.hn digital broadband (DBB) processors provide high-speed networking with 1 Gbits per second maximum throughput over power lines and 1.7Gbits per second maximum throughput over coaxial cables and phone lines. The DBB processors feature a G.hn physical layer (PHY), G.hn datalink layer (DLL), and an embedded CPU for management and control functions. The G.hn analogue front ends provide up to 2Gbits per second physical data rates over any wired medium.

The processors are housed in a small 4 × 4 mm QFN package, the devices deliver programmable transmission and reception gains for each wired medium.

A set of software development kits (SDKs) enable designers to create custom solutions that meet requirements such as IPv4/IPv6 support, quality of service (QoS), and TR-069 management. Engineers can also use the SDKs to develop customised applications that run on a DBB processor’s embedded CPU.

Mouser also stocks three G.hn Wave-2 networking evaluation kits, for a power line, coaxial, or phone line medium. Each kit includes a DBB processor, analogue front end and two evaluation boards that allow designers to evaluate the performance of G.hn networking technology.

MaxLinear was founded in 2003. The company delivers broadband and networking semiconductors based on its integrated radio frequency analogue technology, high-performance optical networking technology. It pioneered MoCA (Multimedia over Coax) and direct broadcast satellite ODU single-wire technology.

Mouser claims to stock the world’s widest selection of the latest semiconductors and electronic components for the newest design projects. Its’ website is continually updated and offers advanced search methods to help customers quickly locate inventory. Mouser.com also houses data sheets, supplier-specific reference designs, application notes, technical design information, and engineering tools.

http://www.mouser.com

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Rutronik UK offers Intel’s Neural Compute Stick 2 for AI

Wed, Jan 23, 2019

Able to bring greater intelligence to network edge devices, the Intel Neural Compute Stick 2 (NCS2) is designed to bring artificial intelligence (AI) algorithms and for prototyping computer vision at the network edge. It is now available from Rutronik UK.

The Intel NCS2 is an affordable way to accelerate the development of deep neural networks inference applications and delivers a performance boost compared with the earlier neural compute stick.

The Intel NCS2 is powered by the latest generation of Intel vision processing unit (VPU), the Intel Movidius Myriad X VPU. According to Rutronik, this is the first VPU that features a neural compute engine for delivering additional performance. Deep learning neural networks like Caffe, Tensor Flow or MXNet can be integrated with the OpenVINO toolkit on the NCS2. These machine learning frameworks are optimised for the entirely new deep neural network (DNN) inferencing engine, which delivers eight times the performance of the previous generation device.

Developers can develop AI and computer vision applications and have them operating in minutes using just a laptop and the Intel NCS2, explains Rutronik. The Intel NCS2 runs on a standard USB 3.0 port and requires no additional hardware, enabling users to seamlessly convert and then deploy PC-trained models to a range of devices and without internet or cloud connectivity. Potential uses for the NCS2 is to develop a smart camera, a drone with gesture-recognition capabilities, an industrial robot, or smart home devices.

The NCS2 is available immediately.

http://www.Rutronik24.com

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Super-junction MOSFETs minimise power dissipation

Tue, Jan 22, 2019

Leveraging ST Microelectronics’ carrier-lifetime control technology, the MDmesh DM6 MOSFETs are 600V devices that contain a fast recovery body diode to bring the performance advantages of ST’s super-junction technology to full and half-bridge topologies, zero voltage switching (ZVS) phase shift converters and any topologies that require a diode able to handle dynamic dV/dt.

The MDmesh DM6 MOSFETs have reduced reverse-recovery time (trr) to minimise power dissipation in the diode when turning off after freewheeling. Recovery softness is optimised to enhance reliability, adds ST. In addition, very low gate charge (Qg) and on-resistance (RDSON), and a capacitance profile tailored for light loads, allow higher operating frequencies and greater efficiency, with simplified thermal management and reduced EMI. These characteristics make the MDmesh DM6 devices suitable for equipment such as charging stations for electric vehicles, telecom or data centre power converters, and solar inverters, where they are claimed to enable superior energy ratings with more robust performance and increased power density.

The MOSFETs are part of the STPower portfolio. There are 23 part numbers covering current ratings from 15 to 72A, with gate charge (Qg) ranging from 20nC to 117nC and RDSON from 0.240 down to 0.036 Ohm respectively.

Package options include the new low-inductance leadless TO-LL, PowerFLAT 8×8 HV, D2PAK, TO-220, and TO-247 with short leads, long leads, or Kelvin pin for applications requiring precision current sensing.

The MDmesh DM6 family is in production now. Please contact your local ST office for pricing and sample requests.

http://www.st.com

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Renesas uses AI for motor-driven home appliance maintenance

Tue, Jan 22, 2019

Renesas Electronics introduces its Failure Detection e-AI Solution for motor-equipped home appliances, featuring the Renesas RX66T 32-bit microcontroller. It simplifies home appliance maintenance and makes it easy to add abnormality-detection AI to the microcontroller for failure detection and predictive maintenance.

Embedded AI (e-AI) enables failure detection of home appliances, such as refrigerators, air conditioners, and washing machines, due to motor abnormality. Property data showing the motor’s current or rotation rate status can be used directly for abnormality detection, making it possible to implement both motor control and e-AI–based abnormality detection with a single microcontroller, says Renesas. Using the RX66T eliminates the need for additional sensors, and reduces the bill of materials cost.

When a home appliance malfunctions, the motor operation typically appears abnormal when running and being monitored for fault detection in real-time. Implementing e-AI-based motor control-based detection means that failure detection results can be applied not only to trigger alarms when a fault occurs, but also for preventive maintenance. e-AI can estimate when repairs and maintenance should be performed, and it can identify the fault locations to boost maintenance efficiency and improve product safety by adding functionality that predicts faults before they occur.

The Renesas Failure Detection e-AI Solution for motor-equipped home appliances can control up to four motors by virtue of using the RX66T. Today’s washing machines typically incorporate three motors, one to rotate the washing tub, one to drive the water circulation pump, and one to drive the drying fan. The Renesas Failure Detection e-AI Solution can be used to control these three motors with a single RX66T chip while at the same time monitoring all three motors for faults.

The e-AI solution uses the Renesas Motor Control Evaluation System and an RX66T CPU card. This hardware is combined with a set of sample program files that run on the RX66T as well as a GUI tool that enables collecting and analysing property data indicating motor states. To detect faults, it is necessary to learn the characteristics of the normal state. System engineers can use the GUI tool to begin developing AI learning and optimised fault detection functionality. Once the AI models are developed, the e-AI Translator, e-AI Checker, and e-AI Importer which make up the e-AI solution, can be used to import the learned AI models into the RX66T.

http://www.renesas.com

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