The Kynix Blog

Bird’s new Wideband Power Sensor series of USB Thruline power meters feature five models each suited to a particular application. All capable of measuring True Average Power, Peak Power and Duty Cycle, as well as VSWR/Return Loss, Average Burst Power and CCDF, the WPS series will work with any modulation scheme.The vast majority of RF power meters on the market today, in the milliwatt range, are all focussed on measuring power levels of typically -10dBm +/- 30dB. However, Bird Technologies are one of the few manufacturers to offer RF enquirers equipment capable of measuring “real world” transmitter power levels without the need to use directional couplers or high power attenuators.These new USB Power Meters for “real world” RF power measurements cover; 350MHz to 4GHz (150mW to 150W); 350MHz to 4GHz (25mW to 25W); 25MHz to 1GHz (500mW to 500W); 150MHz to 4GHz (100mW to 25W) and 25MHz to 1GHz (100mW to 100W).Insertion loss is less than 0.1dB (typically 0.05dB) with a VSWR of 1.1:1max (typically 1.05:1), plus a directivity specification of typically 30dB. These parameters contribute to an average power accuracy for all models of ±4% of reading, or 0.17dB, over the full power range at +15 to +350C.All Bird Wideband Power Sensors come with ‘Virtual Power Meter’ software to allow connection to a PC. In addition the WPS will interface with the Bird 5000-XT Digital Power meter, or the majority of the Bird SA / SH series of Site Analysers or SignaHawks.Also announced is the new 7020 Power Sensor, a low cost USB Power Meter similar in operation to the 501XB range. The 7020 contains the same ‘True Average Power’ measurement capabilities within the frequency range of 350MHz to 4GHz (0.15W to 150W), and has an identical accuracy of reading at ±4% +0.05W, or 0.17dB. The 7020 Power Sensor is an ideal low cost, but accurate, USB power meter for many applications.Reference:1005919-1PCUC30M72AV  

The latest buzz in the information technology industry regards "the Internet of things"—the idea that vehicles, appliances, civil-engineering structures, manufacturing equipment, and even livestock would have their own embedded sensors that report information directly to networked servers, aiding with maintenance and the coordination of tasks.Realizing that vision, however, will require extremely low-power sensors that can run for months without battery changes—or, even better, that can extract energy from the environment to recharge.Last week, at the Symposia on VLSI Technology and Circuits, MIT researchers presented a new power converter chip that can harvest more than 80 percent of the energy trickling into it, even at the extremely low power levels characteristic of tiny solar cells. Previous experimental ultralow-power converters had efficiencies of only 40 or 50 percent.Moreover, the researchers' chip achieves those efficiency improvements while assuming additional responsibilities. Where its predecessors could use a solar cell to either charge a battery or directly power a device, this new chip can do both, and it can power the device directly from the battery.All of those operations also share a single inductor—the chip's main electrical component—which saves on circuit board space but increases the circuit complexity even further. Nonetheless, the chip's power consumption remains low."We still want to have battery-charging capability, and we still want to provide a regulated output voltage," says Dina Reda El-Damak, an MIT graduate student in electrical engineering and computer science and first author on the new paper. "We need to regulate the input to extract the maximum power, and we really want to do all these tasks with inductor sharing and see which operational mode is the best. And we want to do it without compromising the performance, at very limited input power levels—10 nanowatts to 1 microwatt—for the Internet of things."The prototype chip was manufactured through the Taiwan Semiconductor Manufacturing Company's University Shuttle Program.Ups and downsThe circuit's chief function is to regulate the voltages between the solar cell, the battery, and the device the cell is powering. If the battery operates for too long at a voltage that's either too high or too low, for instance, its chemical reactants break down, and it loses the ability to hold a charge.To control the current flow across their chip, El-Damak and her advisor, Anantha Chandrakasan, the Joseph F. and Nancy P. Keithley Professor in Electrical Engineering, use an inductor, which is a wire wound into a coil. When a current passes through an inductor, it generates a magnetic field, which in turn resists any change in the current.Throwing switches in the inductor's path causes it to alternately charge and discharge, so that the current flowing through it continuously ramps up and then drops back down to zero. Keeping a lid on the current improves the circuit's efficiency, since the rate at which it dissipates energy as heat is proportional to the square of the current.Once the current drops to zero, however, the switches in the inductor's path need to be thrown immediately; otherwise, current could begin to flow through the circuit in the wrong direction, which would drastically diminish its efficiency. The complication is that the rate at which the current rises and falls depends on the voltage generated by the solar cell, which is highly variable. So the timing of the switch throws has to vary, too.Electric hourglassTo control the switches' timing, El-Damak and Chandrakasan use an electrical component called a capacitor, which can store electrical charge. The higher the current, the more rapidly the capacitor fills. When it's full, the circuit stops charging the inductor.The rate at which the current drops off, however, depends on the output voltage, whose regulation is the very purpose of the chip. Since that voltage is fixed, the variation in timing has to come from variation in capacitance. El-Damak and Chandrakasan thus equip their chip with a bank of capacitors of different sizes. As the current drops, it charges a subset of those capacitors, whose selection is determined by the solar cell's voltage. Once again, when the capacitor fills, the switches in the inductor's path are flipped."In this technology space, there's usually a trend to lower efficiency as the power gets lower, because there's a fixed amount of energy that's consumed by doing the work," says Brett Miwa, who leads a power conversion development project as a fellow at the chip manufacturer Maxim Integrated. "If you're only coming in with a small amount, it's hard to get most of it out, because you lose more as a percentage. [El-Damak's] design is unusually efficient for how low a power level she's at.""One of the things that's most notable about it is that it's really a fairly complete system," he adds. "It's really kind of a full system-on-chip for power management. And that makes it a little more complicated, a little bit larger, and a little bit more comprehensive than some of the other designs that might be reported in the literature. So for her to still achieve these high-performance specs in a much more sophisticated system is also noteworthy."Related products:XC7Z100-2FFG900IXC7Z010-1CLG400IA2F200M3F-1FGG256 

A bump circuit with flexible tuning ability that uses 500 times less power and is smaller than previous circuits has been demonstrated by researchers at the University of Tennessee in the US."The challenges and requirements of the analogue deep-learning system inspired us to come up with this radically new design," said Junjie Lu, the lead author. "We implemented the bump circuit by preceding the current correlator with a novel nano-power tunable transconductor to achieve variable width and height. By significantly reducing the power consumption of the bump circuit, this work makes possible the realisation of analogue learning and signal processing systems that achieve better energy efficiency than their digital equivalents, and ultimately fully autonomous systems, which are able to get both information and energy from the environment without external intervention."Towards flexible transferThe bump circuit is a family of circuits with bell-shaped, non-linear transfer functions. First appearing in 1991, they are widely used to provide similarity or distance measures in analogue signal processing systems such as support vector machines, neural networks and analogue machine-learning systems.The original bump circuit design lacked the ability to change the width of its transfer function, which is desirable in many applications to represent distributions with different variance or templates with different model parameters. A common approach to solve this is to pre-scale the input voltage, but the circuits required are physically large and consume a lot of power. Other approaches also have limitations such as complex circuitry, large physical size, and a restricted number of possible widths achievable.Hidden depthsThe researchers from the University of Tennessee designed their circuit as an important building block in an analogue deep-learning machine, which is able to perform unsupervised learning and extract salient features from high-dimensional input data, with a much better power efficiency than the existing digital machine learning implementations.Large-scale systems require the computational element, or bump circuit in this case, to be very efficient in both power and area. It is also important that the output features, which are the confidence scores that the current input belongs to each of the previous observations, take both the mean distances and probabilistic variances into account. A bump circuit that has a tunable centre for mean tuning, width for variance tuning, and height for normalisation is therefore highly desirable, and if these three bump parameters can be individually tuned and controlled by a single signal, this would greatly help with on-chip trainability.To achieve the variable height and width, the researchers designed and incorporated a novel transconductor, linearised using the drain resistances of saturated transistors. They adopted a pseudo-differential structure to allow operation with a low supply voltage, and designed a common mode feedback circuit to provide common mode rejection for the pseudo-differential structure to get a tunable bump height.The whole circuit uses 18.9 nW power from 3 V supply which is 1/500 th of the power of the next best bump circuit with tunable width. Implemented in 0.13 µm CMOS, it is smaller in area by 6%, and has maximum flexibility through the individual tunability of the three key bump function parameters. Another feature is that multiple bump circuits can be easily cascaded to represent multivariate probability.A vision of the futureWith power scaling in CMOS tapering off, there has been renewed interest in analogue computation recently, and the researchers expect to see some very exciting results in this area. They are currently working to integrate low-power circuits, such as their bump circuit, into larger systems for real-world applications."One application area we've been working on is machine vision," said Lu. "We've been working with image processing and machine vision researchers to build a complete pipeline using analogue circuits. This circuit helps to provide a path to implementing multi-dimensional kernel methods for machine learning."Systems using the bump circuit could find application in many areas such as healthcare monitoring, environmental monitoring, process control and battlefield surveillance. In addition, the nano-power tunable linear transconductor developed in this work, which has the advantages of ultra-low power, large input range and gm tunability, could be used in a huge range of applications such as amplifiers, filters and oscillators.Related products:LMV1031UR-20LM4889MALM4867MTE

Parts fail and things break. It's a fact of life and engineering. Some component failures can be avoided by good design practices, but many are out of the hands of designers. Identifying the offending component and why is might have failed is the first step to refining the design and increasing the reliability of a system that has been experiencing component failures.How Components FailThere are numerous reasons for why components fail.Some failures are slow and graceful where there is time to identify the component and replace it before it fails completely and the equipment is down. Other failures are rapid, violent, and unexpected, all of which are tested for during product certification testing. Some of the most common reasons for components to fail include:Over currentOver voltageOver temperatureConnected incorrectlyChange in operating environmentManufacturing defectMechanical shockMechanical stressRadiationContaminationPackagingConnectionsAgingCascading failureCorrosionRustingOxidizingThermal runawayLoose connectionsElectroStatic Discharge (ESD)Electrical stressBad circuit design Component failures do follow a trend. In the early life of an electronic system, component failures are more common and the chance of failure drops as they are used. The reason for the drop in failure rates is that the components that have packaging, soldering, and manufacturing defects often fail within minutes or hours of first using the device. This is why many manufacturers include a several hour burn in period for their products.This simple test eliminates the chance a bad component can slip through the manufacturing process and result in a broken device within hours of the end user first using it.After the initial burn in period, component failures typically bottom out and happen randomly. As components are used or even just sit, they age.Chemical reactions reduce the quality of the packaging, wires, and the component, and mechanical and thermal cycling take their toll on the mechanical strength of the component. These factors cause failure rates to continuously increase as a product ages. This is why failures are often classified by either their root cause or by when the failed in the life of the component.Identifying a Failed ComponentWhen a component fails there are a few indicators that can help identify the component that failed and aid in troubleshooting electronics. These indicators are:Visible-The most obvious indicator that a specific component has failed is through a visual inspection. Failed components often have burnt or melted areas, or have bulged out and expanded. Capacitors are often found bulged out, especially electrolytic capacitors around their metal tops. IC packages often have a small hole burned in them where the hot stop on the component vaporized the plastic around the hot spot all the way through the IC package.Smell- When components fail, a thermal overload often occurs which causes the magic blue smoke and other colorful smoke to be released by the offending component. The smoke also has a very distinct smell and varies by type of component. This is often the first sign of a component failure beyond the device not working. Often the distinct smell of a failed component will stay around the component for days or weeks which can aid in identifying the offending component during troubleshooting.Sound- Sometimes components make a sound when they fail. This happens more often with rapid thermal failures, over voltages, and over current events. When a component fails this violently, a smell often accompanies the failure. Hearing a component fail is rarer, and it often means that pieces of the component will be found loose in the product so identifying the component that failed may come down to finding which component is no longer on the PCB or in the system.Testing- Sometimes the only way to identify a component that has failed is to test individual components. This can be very challenging on a PCB since often other components will influence the measurement since all measurements involve applying a small voltage or current, the circuit will respond to it and readings can be thrown off. If a system uses several subassemblies, often replacing subassemblies is a great way to narrow down on where the issue with the system is located. 

Inside a secretive AI nonprofit backed by Elon Musk and other Silicon Valley figures, a handful of robots designed to help out in warehouses are gradually learning how to do useful household chores.OpenAI, which was created to do basic AI research, is reprogramming robots developed by Fetch Robotics, a company that supplies warehouse automation hardware. Researchers at OpenAI are equipping the robots with software that lets them train themselves through trial and error.The effort reflects a bet that innovations in software and machine learning, rather than breakthroughs in hardware, are the way to give robotics remarkable new capabilities. Fetch makes a range of robots for warehouses, including systems that follow workers around a building, carrying items dropped into a basket. OpenAI is using a system that features a mobile base but also 3-D depth sensors, a 2-D laser scanner, and a robotic arm with seven degrees of freedom.In April, OpenAI recruited Pieter Abbeel, a professor at the University of California, Berkeley, and a leading expert on robot learning. Abbeel has shown how robots can use a machine-learning approach called deep reinforcement learning to acquire completely new skills that would be hard to program by hand, such as folding towels or retrieving items from a refrigerator. Google DeepMind, an AI subsidiary based in the U.K., uses this technique to get computers to play computer games at a superhuman level (see “Google’s AI Masters Space Invaders”).Abbeel’s robots learn tasks from scratch, using a neural network that receives sensor input and controls physical movement. The network adjusts its parameters automatically as it inches closer to its goal. A robot might try thousands of grips, for instance, in the process of learning how to hold a certain object.“If this goal can be achieved, then there will be economic and industrial benefits,” says Marc Deisenroth, an expert on reinforcement learning at Imperial College London. “Imagine a Roomba not only cleaning your floor but also doing the dishes, ironing the shirts, cleaning the windows, preparing breakfast.”Deisenroth says using off-the-shelf robots could drive costs down. “Currently, the software seems to be the bottleneck,” he adds. “However, independent of this, better hardware could also lead to substantial improvements.” Soft manipulators and elastic feet similar to a monkey’s feet are concepts that researchers have started working on, he says.Some manufacturers, including the Japanese company Fanuc, are testing reinforcement learning as a way to train industrial robots quickly in new tasks such as learning to grasp unfamiliar objects. When many robots work in parallel, the training time required is reduced accordingly. Robot researchers at Google are testing similar learning techniques.“Moving away from having to program robots by hand by endowing robots to learn autonomously is a key element for the future of robotics,” says Jens Kober, an expert on robot learning at Delft University of Technology in the Netherlands. Kober says having robots share the information they have learned will be crucial.While robots such as those made by Fetch are finding their way into many factories and warehouses, domestic robot helpers remain the stuff of science fiction. Performing seemingly simple tasks like washing dishes or folding laundry in a messy home setting is incredibly hard for a machine. A robot programmed the conventional way can easily be thrown off by an unfamiliar object or a slight variation in lighting.OpenAI confirmed that it is working with the robots from Fetch, but it declined to comment further. Melonee Wise, the company’s founder, couldn’t be reached for comment (see “Innovators Under 35: Melonee Wise”).OpenAI was created by Musk and a handful of well-known (and well-heeled) Silicon Valley entrepreneurs, including investor Peter Thiel, Y Combinator president Sam Altman, and the incubator’s cofounder Jessica Livingston. The nonprofit’s backers have committed $1 billion in funding to the project, and it is being led by Ilya Sutskever, a prominent AI researcher who left Google to join the project, and Greg Brockman, an early employee at the high-profile digital payment company Stripe.While OpenAI has committed to making the technology it develops publicly available, it could certainly benefit companies backed by Musk and Thiel, as well as those emerging from Y Combinator. 

The electronics world has been dreaming for half a century of the day you can roll a TV up in a tube. Last year, Samsung even unveiled a smartphone with a curved screen—but it was solid, not flexible; the technology just hasn't caught up yet.But scientists got one step closer last month when researchers at the U.S. Department of Energy's Argonne National Laboratory reported the creation of the world's thinnest flexible, see-through 2-D thin film transistors.These transistors are just 10 atomic layers thick—that's about how much your fingernails grow per second.Transistors are the basis of nearly all electronics. Their two settings—on or off—dictate the 1s and 0s of computer binary language. Thin film transistors are a particular subset of these that are typically used in screens and displays. Virtually all flat-screen TVs and smartphones are made up of thin film transistors today; they form the basis of both LEDs and LCDs (liquid crystal displays)."This could make a transparent, nearly invisible screen," said Andreas Roelofs, a coauthor on the paper and interim director of Argonne's Center for Nanoscale Materials. "Imagine a normal window that doubles as a screen whenever you turn it on, for example."To measure how good a transistor is, you measure its on-off ratio—how completely can it turn off the current?—and a property called "field effect carrier mobility," which measures how quickly electrons can move through the material."We were pleased to find that the on/off ratio is just as good as current commercial thin-film transistors," said Argonne postdoctoral scientist and first author Saptarshi Das, "but the mobility is a hundred times better than what's on the market today."The team also tried bending the films to test what happens under stress. In most thin film transistors, the material starts to crack, which, as you might imagine, affects performance. "But in ours, the properties didn't change at all," Roelofs said. "The layers just slide and don't crack."The transistors also maintained performance over a wide range of temperatures (from -320°F to 250°F), a useful property in electronics, which can run very hot.To build the transistors, the team started with a trick that earned its original University of Manchester inventors the Nobel Prize: using a strip of scotch tape to peel off a sheet of tungsten diselenide just atoms thick."We chose tungsten diselenide because it provides the electron and hole conduction necessary for making transistors with logic gates and other p-n junction devices," said Argonne scientist and coauthor Anirudha Sumant.Then they used chemical deposition to grow sheets of other materials on top to build the transistor layer by layer. The final product is 10 atomic layers thick. (See sidebar for an illustration).Next, the team is interested in adding logic and memory to flexible films, so you could make not just a screen but an entire flexible and transparent TV or computer."However, more work needs to be done in developing large-area synthesis of tungsten selenide to realize the true potential for applications of our work," said Sumant.