Новини світу мікро- та наноелектроніки

Recently there have been a series of design ideas published based on the topic of “processors” of PWM signals. The purpose of these processors is to minimize both the settling time in response to a PWM duty cycle change and the residual PWM ripple. In many cases the simpler processors—which consist only of a low pass filter built with resistors and capacitors—perform well (Figure 1).

Wow the engineering world with your unique design: Design Ideas Submission Guide

Figure 1 Simple PWM process with a low pass filter built with resistors and capacitors, this filter structure can implement real poles only.

 However, there has been little discussion on how to select optimal filter component values for PWMs of arbitrary characteristics, let alone for the specific PWM (8-bit, 1MHz clock) mentioned in one idea. In this article, I’ll describe a method for the optimization of these simpler filters for PWMs of arbitrary periods.

Let’s start with some nomenclature: we’ll characterize the unfiltered PWM as having B bits, a duty cycle d, a period T, and (unitless) output values of either 0 or 1. (Warning: If you just want the solution to the problem and don’t want to experience the thrill of derivation and the agony of the math, skip to the “Implementing the solution” section at the end of this post.)

For any third order filter with negative, unequal poles p1, p2, and p3, the time domain response to a unit step is:

The response is different if two or three poles are equal. For three, it’s:

One of our interests is in the time (ts) it takes to settle to a specified fraction F of 1 (the worst case of a full scale PWM transition from d = 0 to 1). It is preferable to deal with the amplitude settling error (ASE), h(t, p1, p2, p3) = 1 – hh(t, p1, p2, p3). The ASE is completely settled (goes to zero) at time t = infinity. We might ask the value of ts such that h(ts,p1,p2,p3) = F = 2-B-1, or ½ LSB of the PWM. Of course, other choices can be made if an application produces smaller maximum duty cycle steps.

We’re also interested in the amplitude of the filtered PWM ripple. The filter’s frequency response is:

where j = (√-1) and ω is the frequency in radians/second (rad/sec).

The unfiltered PWM output can be represented as the sum of an infinite number of sinusoids of frequencies which are integral multiples of 1/T = ωPWM/(2π) Hz. The highest amplitude sinusoid that can be produced has a peak magnitude of 2/π when d = 50% and a frequency of ωPWM rad/sec. The entire PWM signal passes through a low pass filter which attenuates the higher multiples more than the first. Under these conditions, the output of a reasonably good low pass filter consists almost entirely of the frequency of ωPWM. It’s apparent that excessive ripple and excessive settling times are equally capable of ruining your day. So we seek to know the ωPWM and ts for filters such that the ASE and the ripple amplitudes are both equal to F, that is, they meet “the criteria” that H(ωPWM, p1, p2, p3) = π∙HH(ωPWM, p1, p2, p3)/2 = F and F = h(ts, p1, p2, p3). Of course, we also want to find filters which give us the smallest ts and ωPWM for a given value of F.

Let’s start by looking at the specific case of a PWM with a value T = 28/1 MHz = .000256 for which ωPWM = 2π/T  = 24543. An obvious starting point is a filter of equal-valued resistors and capacitors. For the Figure 1 circuit, the frequency response transfer function is:

Setting every R to 1 Ω and every C to 1 F, a polynomial roots finder routine can be used to determine the three poles and the value of ω that satisfy H() = F = 2-8-1: -3.247, -1.555, -.1981 and 9.0699. To get the same attenuation at a frequency ωPWM, the poles are multiplied and the resistors divided by FSF = ωPWM/ω. Of course, these resistors and the 1 F capacitors are inconvenient to work with, to say the least. So, we can choose an impedance scale factor ZSF such as 10-8 to multiply the capacitances and divide the resistances. The results are 37.0k (select the nearest standard value) and 10n. (Applying a ZSF has no effect on the filter’s response.) Knowing the poles for the 1 Ω / 1 F filter and requiring that h( ) = F, a root finder routine also gives us a value of ts =  32.5 s. Dividing ts by FSF maintains the same F and results in a value of ts equal to 12.01 ms.

Of course, there is no reason to expect that equal R’s and C’s will produce a filter that yields the lowest possible ts and ωPWM for a given value of F. How shall we search for a better filter? We use Monte Carlo. Starting with FSF-scaled values of the poles above, a new, better pole set is selected only if it reduces the value of either H( , , , ωPWM) or h(, , , ts) without increasing that of the other. A 10 million sample Monte Carlo with sets of randomly chosen poles was run. The result was poles at -2290.7, -2238.9 and -2218.6, a better ripple attenuation of .001938 < F, and a vastly improved ASE of 7.3834∙10-10. Clearly, the best choice is identical poles, sometimes referred to as synchronous ones. Unfortunately, it’s impossible to implement synchronous poles with the Figure 1 network. But we can get close if we set R3 = K∙R2 = K2∙R1 and C1 = K∙C2 = K∙C3, with K being a value greater than unity. The larger K is, the better. Of course, there are obvious practical limits to the value of K, but let’s look at cases where K is 1, √10, 10, 100 and infinity. See Figure 2, which shows the time domain responses of filters which meet “the criteria”.

Figure 2 The ASE is shown for a full scale PWM transition verses time for various filter types. The horizontal line corresponds to an ASE of 2-9.

The dashed horizontal line is at a value of F = 2-9. Each of the other curves corresponds to the ASE of a filter whose H( , , , ωPWM) is also F. The curves intersect the horizontal line at the time when the filter’s ASE falls to F. The worst performer is the red curve with equal R’s and C’s for K = 1. The yellow and green, for K = √10 and 10 are better and seem practical, but the blue K = 100 filter requires impedance ratios of K2 = 10000. And the violet K = infinity curve is unrealizable… or is it?

The filters of the Figure 1 network will degrade in accuracy when driving resistive loads. The simplest solution is to buffer their outputs with an op-amp in the voltage follower configuration. Using this op-amp has another big advantage: the filter configuration in Figure 3 expands the number of realizable filter types to include not only the equal pole version, but an even better performer—a complex pole filter that has been determined by trial and error, starting with the poles of a Bessel filter. The “better” poles are: -.84668, -.786203+.725726∙√-1 and -.786203-.725726∙√-1. This filter’s R values can be scaled so that its attenuation H( , , , ) at ωPWM is F. The scaled filter’s performance is reflected by the black curve. Why the odd shape? The complex poles yield a time response which consists of damped oscillations. It passes through zero repeatedly as it settles. What is graphed is the absolute value of the response.

Figure 3 Image of filter structure that can implement real poles (some or all of which can be identical) and complex ones. For the component values shown, the parameter values in each given row of the complex poles section of Table 1 are satisfied (see text).

You might think that these graphs show the innate superiority of the complex pole filter. But they only represent the case where “the criteria” are met for F = 2-9. What about for other values of F? What about other PWMs with values of ωPWM? Here’s an answer. PWM’s have an integer number of bits, so it makes sense to consider values of F = 2-N only for N being a set of positive integers. For each 2-N and each filter under consideration, we can identify values of ω and ts which meet “the criteria”. Knowing ω, an FSF can be calculated for any desired PWM’s ωPWM, and that FSF can also be used to determine the values of the scaled ts and the filter R’s. In a filter with the FSF-scaled poles, ωscaled = FSF∙ω and ts-scaled = ts/FSF. And so regardless of the PWM frequency ωPWM, the product of ωPWM and the FSF-scaled ts will remain constant. The smaller the value of this product, the better. We can compare these products for the complex and synchronous filters to determine which is the better choice for each value of F. Refer to Table 1.

Table 1 Values of ω, ts and ω∙ts of the complex and synchronous filters for various values of F = 2-N.

The comparison reveals that for every F, the complex filter has the smaller value product and is the better choice. We can now generalize the filter design procedure.

Implementing the solution

A specific example will illustrate the solution to the general problem. Assume a PWM where B = 8 and T = 2B/1 MHz = .000256. We want a ripple level and ASE of F = 2-9. Figure 3 shows the filter component values for the complex filter in Table 1. For N = 9, the filter gives a frequency of ω = 9.1868 for that value of F. But we want that attenuation to be at a frequency of ωPWM = 2π/T. We need to divide the filter’s resistors and the Table’s ts = 6.3876 by an FSF = ωPCM/ω = 2671.7. This yields R1 = 66.527 kΩ, R2 = 45.445 kΩ and R3 = 178.95 kΩ (you can use the nearest standard values) and ts = 2.39 ms. You might also choose to scale these capacitors and resistors by a constant ZSF, multiplying the resistors and dividing the capacitors by that value. The ZSF manipulation has no effect on the filter responses.

It should be noted that for values of N = 6 or less, the synchronous filter has a smaller, better value of ω than the complex one, and that for larger values of N, the values of ω are almost identical. The complex filter is still the better choice though; FSF’s can be calculated with values of ω in the denominator that are between pairs in the table. Making ω larger will increase ripple attenuation and the settling time. A value of FSF can always be found that will lead to smaller scaled filter values of ω and ts than those offered by the other filters.

 Designing a filter for a PWM of arbitrary frequency

A method using an op amp and three pairs of resistors and capacitors has been presented for designing a filter for a PWM of arbitrary frequency and number of bits. This filter limits both the ASE and peak ripple to F, a user-selected negative integer power of 2. Filters with poles having various interrelationships were investigated. The selected complex-pole filter has the smallest product of frequency and settling time among those considered. Using Table 1 and Figure 3, the filter components can be scaled to the desired value of F for a PWM of any frequency. The same scaling can be applied to the settling time listed in the table to calculate the scaled settling time.

If you prefer an op amp-free solution, you might want to consider a K = 10 version of the Figure 1 circuit. With R1 = 4.3k and C1 = 100n, for an F = 2-9, the ts is approximately 4.6 ms that you see for the green curve in Figure 2. That filter’s ω is 15787 rad/sec for the same F. I have not provided a Table for this filter, but you can test the results in a circuit simulator when applying different FSF’s to the filter resistors.

Acknowledgements

I’d like to thank David Lundquist for the review of and the valuable suggestions that he provided for this article. Any errors that might have escaped notice are of course exclusively my own.

Christopher Paul has worked in various engineering positions in the communications industry for over 40 years.

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Large semiconductor suppliers have started acquiring machine learning (ML) software houses in a bid to bolster their artificial intelligence (AI) offerings for embedded systems, and the latest transaction has been signed between Infineon Technologies AG and Imagimob AB, a Stockholm-based startup that provides ML solutions for edge devices. The Swedish company’s toolchain delivers production-grade ML models.

This platform can be used in a variety of sensor and Internet of Things (IoT) use cases like audio event detection, voice control, predictive maintenance, gesture recognition, and signal classification. So, at a time when AI/ML technologies are penetrating almost every embedded system, Infineon aims to leverage this AI edge platform for its sensor and IoT solutions. Moreover, this Tiny Machine Learning technology will boost Infineon’s hardware/software ecosystem for embedded systems.

Figure 1 Infineon’s acquisition is aimed at the adoption of Tiny Machine Learning in IoT applications. Source: Imagimob

Infineon’s European neighbor STMicroelectronics signed a similar deal a couple of years ago when it snapped Cartesiam, a software company developing tools for ML and inferencing on Arm-based microcontrollers. The Toulon, France-based Cartesiam was founded in 2016, and its team included data scientists and embedded signal processing experts.

Cartesiam’s NanoEdge AI Studio enabled embedded systems designers without prior knowledge of AI to rapidly develop specialized libraries and integrate machine-learning algorithms directly into a broad range of applications. At the time of acquisition, the French startup’s AI solution was already in production in connected devices, household appliances, and industrial machines.

At ST, it’d complement the Franco-Italian chipmaker’s STM32Cube.AI toolset, which allows design engineers to map and run pre-trained artificial neural networks on the company’s STM32 microcontrollers. Like Infineon’s acquisition of Imagimob, adding Cartesiam’s ML technology is expected to boost ST’s embedded AI offerings at the edge.

Figure 2 Cartesiam’s ML technology at the edge is expected to complement the STM32Cube.AI platform. Source: STMicroelectronics

These two deals underscore two important trends. First, AI/ML technologies are now a crucial part of hardware and software stacks in embedded system designs. Second, as part of their AI strategy, chipmakers are increasingly offering complementary tools alongside semiconductor devices to address the full spectrum of embedded AI/ML learning needs.

So, expect more deals like these in the future.

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R&S’ Interactivity Test has been approved by the ITU as a standardized test procedure to evaluate 5G network performance for real-time, interactive applications. Defined in Recommendation ITU-T G.1051, the Interactivity Test measures round-trip latency, packet delay variations, packet error rates, and proofing bit rates to identify network issues that affect the quality and reliability of real-time and interactive services.

The test methodology measures latency under real network load conditions. The load patterns can be adjusted to specific use cases. All low-layer information on the packet service level is available, and an over-the-top assessment generates a network interactivity score. Information from the test can help identify data transmission bottlenecks.

According to R&S, the Interactivity Test is more granular than legacy data tests. It helps optimize network infrastructure for real-time and interactive use cases, ensuring compliance with industry standards and regulations. Moreover, it aims to improve quality of experience (QoE) and quality of service (QoS).

Interactivity Test can be used with any Rohde & Schwarz active mobile network testing solution. To learn more about Interactivity Test solutions, click here.

Rohde & Schwarz 

Find more datasheets on products like this one at Datasheets.com, searchable by category, part #, description, manufacturer, and more.

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The c-BMS24X battery management system (BMS) from Sensata handles up to 24 cells in series and 2000 A of continuous current. Targeting low-voltage electric vehicles and energy storage systems, the c-BMS24X offers upgraded software functions that improve vehicle range, uptime, and battery health/performance.

Along with BMS-controlled convenience features, such as battery heater management and automatic sleep mode to consume power, the c-BMS24X extended software provides:

• Support for connecting up to 10 battery packs in parallel for flexible battery design and increased safety and serviceability;
• Battery swap functionality in parallel battery packs without authorized personnel, allowing the end user of a motorbike to switch the battery;
• Improved measurement and prediction of state of charge (SOC), state of health (SOH), and state of energy (SOE);
• Added state of power (SOP) estimation for predicting how much power is available for the next 3, 10, and 30 seconds, important for scooters and motorbikes;
• Enhanced balancing for cell chemistries, such as lithium iron phosphate (LFP), used in applications that are never fully charged, including energy storage systems and forklifts that operate 24/7.

Sensata is set to launch the c-BMS24X battery management system, under the brand Lithium Balance, at The Battery Show later this month in Stuttgart, Germany.

c-BMS24X product page

Sensata Technologies 

Find more datasheets on products like this one at Datasheets.com, searchable by category, part #, description, manufacturer, and more.

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Keysight’s USPA platform enables pre-tapeout ASIC and SoC prototyping and verification using integrated digital twins of compliant, standards-based signals. The M8135A/M8136A universal signal processing architecture (USPA) platform provides a real-time development environment that can reduce the risk, cost, and time associated with silicon chip prototyping and verification.

To minimize the risks of design failures and expensive re-spins, USPA employs digital twin signaling to verify designs before they are committed to silicon. Fast ADC and DAC interfaces emulate signals at full speed, up to 68 Gsamples/s and 72 Gsamples/s, respectively, to support optical communication development projects.

The modular FPGA-based prototyping system furnishes a range of I/O interfaces that support 6G wireless development, digital radio frequency memory (DRFM), and physics research. It can also be used for high-speed data acquisition applications, including radar channel emulation and radio astronomy.

USPA is available in two versions. The M8135A is a preconfigured system for single-channel transceiver applications. The M8136A is a fully configurable set of modular components that can be combined to accommodate both single-channel and multichannel operation.

USPA product page

Keysight Technologies 

Find more datasheets on products like this one at Datasheets.com, searchable by category, part #, description, manufacturer, and more.

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Comprising five variants, ThingMagic M7e series UHF RAIN RFID embedded modules from Jadak are among the smallest on the market. The modules, which are based on Impinj E family RAIN RFID reader chips, allow manufacturers to add RAIN RFID functionality to very small products, including handheld RFID scanners, wearables, surgical hand scanners, and mobile printers.

According to Harinath Reddy, senior R&D director, RFID, JADAK, “These new modules are optimized to achieve the highest read/write and thermal performance while consuming low DC power, which is very critical for our supply chain logistics, retail, and healthcare customers. In addition, some of the M7e modules are drop-in compatible with our M6e modules, allowing our customers to easily migrate to the new modules.”

At 18×21×3 mm, the surface-mount M7e-Pico is the smallest module in the series and is capable of reading up to 300 tags/s. It also has an RF output range 0 dBm to +24 dBm. DC power consumption for this device is <2.5 W at 5 V and +24 dBm, dropping to <1.2 W at 5 V and 0 dBm. The largest M7e modules are 46×26×4 mm and deliver read rates of up to 800 tags/s.

All of the M7e modules use the same Mercury API as other JADAK ThingMagic products, enabling easy software integration and customization based on form-fit-function SKUs. Additionally, Jadak works to obtain regulatory certifications for select countries prior to launch, easing the burden on OEMs to obtain their own regulatory certification.

ThingMagic M7e series product page

Jadak

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Guerrilla RF is sampling the GRF2110, a C-band low-noise amplifier for WiFi 6/6E, small cell, wireless infrastructure, and other RF applications up to 8 GHz. This linear amplifier exhibits a noise figure of 1.0 dB and flat gain of 17 dB, while drawing 70 mA of current. It also provides OIP3 linearity of 40 dBm and OP1dB power level of 22 dBm. The GRF2110’s tuning range is 5 GHz to 8 GHz.

“The C-Band is considered waterfront property in terms of RF spectrum because it offers the best combination of RF coverage and RF bandwidth, and cellular operators have been investing heavily in this band,” according to Alan Ake, VP of Multimarket Products at Guerrilla RF. “This band is also used by many other applications such as satellite earth stations; aeronautical navigation; radar; vehicle-to-everything (V2X); Industrial, Scientific, Medical (ISM); and WiFi 6E. Accordingly system designers will need to pay increased attention to RF impairments such as interference, gain compression, receiver saturation, noise floor degradation, and intermodulation.”

According to Ake, the GRF2110 was designed with these C-band–specific impairments in mind, with optimized gain, linearity, and noise figure performance. It also uses the same pin-compatible package as multiple other GRF products, providing design flexibility.

Housed in a 1.5×1.5-mm DFN-6 package, the GRF2110 amplifier operates from a supply voltage of 2.7 V to 5 V with a typical bias condition of 5 V and 70 mA for optimal efficiency and linearity.

GRF2110 product page

Guerrilla RF 

Find more datasheets on products like this one at Datasheets.com, searchable by category, part #, description, manufacturer, and more.

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It has been put forth that automobile “headlights”, to which reference was previously made, are not headlights at all but are “running lights”. In light of that (pun intended), please now see the  pair of photographs in Figure 1: They are still images taken from a video clip.

Figure 1 Two Photographs of a turning automobile where the white lights in one headlight stays on while the other one (with an active turn signal) turns off. Source: John Dunn

These quick succession images were taken on April 17, 2023, at 1 PM while the weather was overcast and cloudy. However, I have seen exactly the same situation depicted above unfold on other occasions, well after sunset with the sky pitch black.

The white light as shown above, which remains lit without interruption, appears to be the output of a headlight. If it is instead a running light, the danger cited here arises anyway.

It should be noted that most cars, to my own observations, do not change the state of their white lights when turn signals are activated, only a few of them do. However, when the white lights of one side of those few vehicles do go dark, much needed illumination of the upcoming vehicle path can be markedly diminished which is very dangerous.

This is only the first peril meriting our concern. There is another as well.

A white light that is glowing all by itself on just one side of a vehicle as depicted above could, if only for a moment, be mistaken by a momentarily stressed and distracted driver of another vehicle as being the single white light of a bicyclist or a motorcyclist. The danger of such a misidentification should be self evident, but to vehemently stress that point, we will go further.

The following vehicle misidentification story dates back to the mid-1960s and was told to me in 1968 by one of my then co-workers. Be warned in advance that it may be very upsetting. Be prepared.

Please see this picture of a vintage Lincoln Continental in Figure 2 extracted from this URL and note the very wide separation between the two sets of headlights. When being driven with the high beams off, only one bulb on each side of the vehicle’s front end gets lit. In the dark at night, these two widely separated lights can be mistaken for two widely separated, side by side vehicles instead of the single vehicle it actually is.

Figure 2 A 1960 Lincoln Continental with a wide separation between two sets of headlights. Source: curbsideclassic.com

A gang of motorcyclists once spotted one of these cars coming toward them on a particularly dark night and they misidentified the widely spaced headlights of an oncoming Lincoln as being two separate lights of two separate oncoming motorcycles.

One of the gang members decided to scare what he thought were two oncoming motorcyclists by riding his own motorcycle head on at high speed straight in between the supposed pair of oncomers.

The story ends there.

John Dunn is an electronics consultant, and a graduate of The Polytechnic Institute of Brooklyn (BSEE) and of New York University (MSEE).

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Artificial intelligence (AI) has spread across so many industry sectors and applications that the change came almost gradually, AI became one of those tools that we search for without thinking about it. From voice assistants to self-driving cars, AI is redefining how we interact with technology. One area where AI has made a significant impact is modern computing. In this blog post, we’ll explore the role of AI in modern computing, understand the synergy between these two domains, and examine a few exciting examples like RPG name generators, recommendation systems and CNNs.

AI and modern computing have evolved hand-in-hand, with advancements in one field propelling the other forward. As computational power increased over time, it allowed researchers to develop more sophisticated AI algorithms. In turn, AI has driven the need for even more powerful and efficient computing technologies.

• One fun and engaging example of AI’s influence on modern computing is the RPG name generator. By leveraging natural language processing algorithms, this tool generates creative and unique names for characters in role-playing games. The generator uses AI to analyze patterns and structures of names from various cultures and fictional works, and then combines them in novel ways. This showcases the versatility of AI and its capacity to create engaging, personalized experiences in the realm of entertainment.

• Another example of Artificial Intellingence functions is the recommendation systems used by streaming platforms like Netflix or Spotify. These systems utilize AI algorithms such as collaborative filtering and matrix factorization to analyze user behavior, preferences, and content metadata. By doing this, they offer unique almost uncanny recommendations for movies, TV series, and music, optimizing user experience and a sense of immersion.

• Following this trend, we find the AI-powered chatbots, which leverage natural language processing and understanding algorithms to provide real-time customer support. By interpreting user input, chatbots can answer questions, resolve issues, or guide users through various processes, all while learning and improving through continued interaction.

• If we go further, we can name the significant strides in the medical field that AI has made with algorithms like convolutional neural networks (CNNs) playing a pivotal role in medical imaging. These networks can analyze images to detect and diagnose diseases, such as cancer, with remarkable accuracy. This application of AI not only streamlines the diagnostic process but also has the potential to save lives by facilitating early detection and intervention.

Now that we have seen some of the most common and not so common ways in which AI works and we probably don’t think about, we can move further into the influence of it in the modern world, more specifically computing.

• Accelerating hardware development: AI has sped up into the world so fast that the need for a specialized hardware that could handle tasks related to AI was imperative. That’s why Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs) now count with a better design to take on tasks efficiently. These advancements have accelerated modern computing capabilities and enabled the development of more complex AI models.

• Enhancing cybersecurity: AI algorithms are playing a crucial role in identifying and mitigating security threats. By analyzing large volumes of data, AI can detect patterns and anomalies, helping to prevent cyber-attacks and protect sensitive information.

• Optimizing resource allocation: AI is being used to optimize computing resources dynamically. By analyzing system performance in real-time, AI can allocate resources efficiently, ensuring that computing systems are running at their optimal capacity.

• Enabling next-generation computer architecture: As a result of AI, the architecture of new computers and the development of these have seen a new surge. The perfect example of this is neuromorphic computing, which mimics the human brain’s structure and function. Now that we have this approach to computing, we can look into the way it’ll revolutionize how much we process and analyze new data, the potential of it is tremendous.

AI has undoubtedly played a significant role in shaping modern computing, with both fields evolving together and pushing the boundaries of what is possible. From accelerating hardware development to enhancing cybersecurity, AI continues to drive innovation in computing technology. Any tech lover or aficionado, can look forward to witnessing even more groundbreaking advancements in AI and modern computing, transforming the way we interact with and benefit from technology.

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“Pulse power” has received a lot of attention and mass-media exposure recently, as demonstrated by the dramatic example in December 2022. That’s when the National Ignition Facility (NIF) at Lawrence Livermore National Laboratory focused 2.05 megajoules (MJ) of light from 192 lasers onto a tiny capsule of fusion fuel, Figure 1. This initiated an explosion in the target that produced 3.15 MJ of energy—more energy at the output than the input, and the first time the output energy exceeded the input. Although the pulse was just a few nanoseconds long, the peak-power number was also impressive at 500 terawatts.

Figure 1 The NIF fusion arrangement is definitely not a benchtop or DIY power project: the 10-meter diameter target chamber gives a sense of the scale for the facility. Source: National Ignition Facility/Lawrence Livermore National Laboratory

There’s another consideration in this story of fusion power and energy input and output: efficiency. The lasers themselves are very inefficient and converted only about 1% of their input power into lased output light.

The NIF project is an extreme example of pulsed power. That phrase usually refers to the science and technology of accumulating energy over a relatively long period of time and releasing it as a high-power pulse composed of high voltage and current but over a short period of time. It often has extremely high power but moderately low energy. Applications of pulsed power include recycling, energy research, laser-based weapons, electromagnetic launchers for aircraft, material processing, medical treatments and systems, and food and agriculture.

Although we tend to associate conventional power sources and supplies with a somewhat more “steady-state” power delivery, there are many applications and situations which call for pulse-like performance. For example, repetitively pulsed lasers used for welding typically need power on the order of one kilowatt with a pulse repetition rate of about 1000 pulses per second.

Designing a supply which can deliver pulsed power, where the output voltage and/or current must ramp from idle to full in a few milliseconds or even microseconds, is a challenge. This pulsed power is usually produced by transferring energy stored in capacitors and inductors to a load very quickly via switching devices, Figure 2, or topologies such as Marx generators.

Figure 2 Pulse power usually starts with energy stored as a voltage in a capacitor (top) or current in an inductor (bottom) and a fast-acting switch to release that energy, but there is obviously much more than that to a viable source. Source: SLAC National Accelerator Laboratory

The challenge of pulse power is not just achieving the balance between accumulating energy and then expending it as power in a brief burst. In most cases, the pulse rise time must be fast (microseconds or faster), so every subtle impediment to slewing, such as parasitic inductance or capacitance, must be identified and minimized. 

There are also many smaller-scale applications which are not formally considered pulse-power situations but have some of their attributes. Consider a high-end processor (video, data, AI, or FPGA) which is in low-power states, then must quicky ramp up to handle a specific task. The current on the low-voltage core’s supply may ramp from tens of milliamps to tens of amps in microseconds and do so without overshoot or ringing. At the same time, the power source here is not a pulse-supply, but a “stiff” conventional power supply with good superior dynamics on its load-side regulation. That’s a challenge.

Note that not all pulse-power applications involve sophisticated designs or scientific experiments; some are pulse-like but on a much smaller scale. I have a small, basic Brother laminated-label maker which uses 8 AA batteries, Figure 3.

Figure 3 This Brother label maker takes 8 AA cells and has a barrel-connector port for an external AC/DC supply. Source: Newegg Inc.

Since I only use it every few months, it seemed prudent to not leave the batteries in, yet putting them in and removing them each time was a nuisance (plus, I had to find the eight batteries!). No problem, I figured: the unit has a barrel connector for an external AC/DC adapter. I went online and bought one rated for 9.5V/1.6A, which claimed to be a fit for this specific unit’s requirements; it had the right voltage, current, and barrel-connector size.

Perhaps I shouldn’t have been surprised, but the label-maker did not work with the adapter. I opened up the label maker so I could access and monitor the delivered supply voltage, Figure 4. (It’s all low voltage, so there was no danger; the only risk was shorting something out and ruining the unit.)

Figure 4 Accessing the power section of the label maker was fairly easy; remove a few screws and separate the unit’s top and bottom halves. Source: Bill Schweber

Its transient response was revealing: when called upon to deliver full current to drive the label-maker’s thermal printhead, the voltage dropped down to a few volts for about 50 milliseconds, then rose to nominal voltage and delivered the needed current. Unfortunately, that 50-ms window is when the printhead is expecting to receive full current for heating.

In contrast, the voltage output of the no-name alkaline AA batteries only dropped to 11.5 V—close to open-circuit nominal of 12.8 V (8 × 1.6 V)—and popped back to 12 V in about 10 msec. In other words, as a voltage source, these batteries were much stiffer and had a better transient response with respect load regulation, droop, and recovery.

For many designers, it’s not so much about pulse power in the classic sense as it is about the dynamics of load regulation. Some of the lessons of pulsed-power supplies such as use of capacitors and inductors to store and release energy do apply but others, such as having a long relaxation time between those high-current demands, may not.

Have you ever had a project where performance was stymied or intermittent due to a supply that could not support the load transients, even though its steady-state specifications were adequate? How did you diagnose the problem and then resolve it?

Bill Schweber is an EE who has written three textbooks, hundreds of technical articles, opinion columns, and product features

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• Power needs go beyond just plain voltage and current

References

NIF/LLNL:

1. Science, “With historic explosion, a long sought fusion breakthrough”, https://www.science.org/content/article/historic-explosion-long-sought-fusion-breakthrough
2. National Academies Press, “Review of the Department of Energy’s Inertial Confinement Fusion Program: The National Ignition Facility”, https://nap.nationalacademies.org/read/5730/chapter/6#22
3. Lawrence Livermore National Laboratory, “The National Ignition Facility: Enabling Fusion Ignition for the 21st Century”, https://typeset.io/papers/the-national-ignition-facility-enabling-fusion-ignition-for-1dtoism225

Pulse Power:

1. Photonics Spectra, “Powering the Pulse: Power Design’s Impact on Laser Performance”, https://www.photonics.com/Articles/Powering_the_Pulse_Power_Designs_Impact_on/p5/vo221/i1429/a68713
2. MDPI, “Practical Design of a High-Voltage Pulsed Power Supply Implementing SiC Technology for Atmospheric Pressure Plasma Reactors”, https://www.mdpi.com/2076-3417/9/7/1451
3. SLAC National Accelerator Laboratory, “Pulsed Power Engineering Basic Topologies”, https://uspas.fnal.gov/materials/09VU/PPE_BasicTopologies.pdf
4. SLAC National Accelerator Laboratory, “Pulsed Power Engineering Introduction” (p. 7,8,9), https://uspas.fnal.gov/materials/09VU/PPE_Introduction.pdf
5. Hackaday, “Pulsed Power And Its Applications”, https://hackaday.com/2017/01/11/pulsed-power-and-its-applications/
6. Springer, “Pulsed Power Technology”, https://link.springer.com/chapter/10.1007/978-4-431-56095-1_2

FPGA and related high-power topics:

1. Microchip/Actel, “Power FAQs”, https://ww1.microchip.com/downloads/aemDocuments/documents/FPGA/ProductDocuments/SupportingCollateral/power_faq.pdf
2. Analog Devices, “FPGA Power System Management”, https://www.analog.com/en/design-notes/fpga-power-sys-mgmt.html
3. Analog Devices, “Care and Feeding of FPGA Power Supplies: A How and Why Guide to Success”, https://www.analog.com/en/analog-dialogue/articles/care-and-feeding-of-fpga-power-supplies-a-how-and-why-guide-to-success.html
4. Xilinx/AMD, “FPGA Power Requirements Overview”, https://www.xilinx.com/video/hardware/fpga-power-requirements-overview.html
5. Xilinx/AMD, “Spartan-6 FPGA Power Management”, https://docs.xilinx.com/v/u/en-US/ug394
6. Texas Instruments, “Power Supply Design Considerations for Modern FPGAs”, https://www.ti.com/lit/an/snoa864/snoa864.pdf

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With the emergence of Matter comes a wide breadth of opportunities to improve Internet of Things (IoT) connectivity and enable new applications and solutions for years to come. Matter is an application layer that allows different products from different vendors using other technologies to work seamlessly together.

The protocol aims to reduce fragmentation between different manufacturers and enhance interoperability between smart home devices and IoT platforms from different vendors. To achieve this goal, Matter will use Internet Protocol (IP) that provides interoperability between devices to define a standard application layer—regardless of the underlying network protocol.

Figure 1 Matter is driving the convergence between the major IoT ecosystems. Source: Silicon Labs

If IoT companies want to stay ahead of the curve in today’s evolving market, they’ll need in-depth expertise in the underlying Matter IoT wireless protocols, including Bluetooth, Thread, and Wi-Fi. So, how exactly can you achieve Matter interoperability regardless of protocol? The answer may lie in bridging.

What’s bridging and why it’s needed

With a large, existing installation base of non-IP based Zigbee, Z-Wave and other wireless IoT protocols, wireless coexistence is necessary and exceedingly complex. There are currently over 2,100 certified non-IP based smart home products on the market supporting a wide range of applications. With the development of the Matter standard, these devices will need to be bridged to ensure compatibility with the new protocol.

Products using Zigbee or Z-Wave—like Philips Hue bulbs, SmartThings sensors, Ring Alarm system, and Ecobee thermostats—are already on shelves and in homes around the world but are not currently compatible with Matter. In general, connecting Zigbee and Z-Wave devices to Matter will involve updating existing hubs or adding a new bridge that supports the new protocol.

Developers need a way to connect Matter to these devices and bring today’s existing smart home devices into the future, so consumers can enjoy the benefits of Matter with their existing products. Even though some companies may choose to upgrade their end devices in-field if they meet the flash and RAM requirements, it will be wiser for IoT providers to bridge already-existing sensor networks with Matter networks because it will provide users with a more intuitive, easier experience.

Thankfully, a growing number of IoT companies plan to introduce bridging products that will help support both deployed Zigbee/Z-Wave products and the newer Matter products which use Bluetooth Low Energy (LE) for commissioning and run over Thread, Wi-Fi, and Ethernet protocols. Let’s take a closer look at how a Matter bridge works.

How Matter bridge works

A Matter bridge works by extending connectivity to non-Matter IoT devices in a Matter fabric and allows consumers to keep using existing non-Matter devices such as Zigbee and Z-Wave devices together with new Matter devices. The bridge connects to each device using their respective protocols, such as Zigbee, Z-Wave, or Bluetooth, and then translates messages between them into a Matter protocol that they can all understand.

So, when one device sends a message to the other, the bridge intercepts it, translates it into the Matter protocol, and then sends it to the receiving device. The receiving device then translates the message back into its own protocol and acts on it accordingly.

Essentially, the Matter bridge acts as a mediator between smart home devices that use different communication protocols. It enables devices to work together seamlessly, regardless of the protocols they use. This is particularly important for consumers, who may have smart home devices from different manufacturers that use different protocols.

With a Matter bridge, they can connect all their devices to a single network and control them from a single app or platform. The diagram below shows both connectivity between Thread network and Matter fabric as well as Matter to non-Matter networks like Z-Wave/Zigbee using a bridge device.

Figure 2 This is how a Matter bridge works by connecting all the devices to a single network. Source: Silicon Labs

Matter over Thread

To enable connecting to Matter over Thread, many smart home device manufacturers are building border routers that support both Thread and Matter protocols. These devices are designed to act as a router between Thread-based smart home devices and Matter-based devices, allowing them to communicate with each other.

What makes Thread unique to other bridges and hubs is its ability to be built into other devices because it is IP based. IoT providers are building a variety of hardware devices that will act as Matter-compatible Thread border routers like smart home hubs, smart speakers, smart locks, and smart lighting to provide users with seamless interoperability across existing Matter and non-Matter devices.

Overall, any device that supports both Thread and Matter has the potential to act as a border router between the two protocols. With the growing popularity of Matter, we are likely to see an increasing number of devices that support both Thread and Matter.

Furthermore, it is possible to combine Thread Border Router functionality with a Matter Bridge to enable support for Matter Thread and non-Matter devices in a single hub.  In fact, some companies are already taking steps to make this a reality. For example, Silicon Labs recently released a fully functional Unify Software Development Kit (SDK), offering bridge solutions for Matter to Zigbee and Matter to Z-Wave, as well connecting to Matter Thread devices.

Figure 3 The SDK offers bridge solutions for Matter to Zigbee, Matter to Z-Wave, and Matter to Thread devices. Source: Silicon Labs

Unify SDK is a software network that simplifies IoT infrastructure development, including application processor-based end products, gateways, hubs, bridges, and access points. Each Unify SDK component implements a Message Queuing Telemetry Transport (MQTT) interface, which is a simple messaging protocol, to the unified language based on Dotdot. As a result, it’s a modular, extendible, lightweight, and well-defined interface for system integration.

Although Unify SDK natively runs on Linux, it is architected for portability. The Unify-Matter bridge application is a part of Unify SDK and is based on CSA’s Matter Bridge Application software. The application receives the Zigbee Cluster Library (ZCL) commands on the Matter protocol interface, translates to Unify Controller Language data model, and publishes to an MQTT interface.

Improving connectivity in future smart homes

By using Thread as the underlying wireless protocol for IoT devices and adding Matter support through border routers, IoT providers are making it easier to create a connected ecosystem of devices that work together. Those devices that do not have native Matter support can still interoperate with Matter through a Matter bridge.

This approach enables consumers to choose from a wider range of devices and manufacturers, while still ensuring compatibility and ease of use. As the industry continues to evolve, we can expect to see even more devices that support both Thread and Matter, making it easier than ever before to create a seamless and connected smart home experience.

Rob Alexander, the principal product manager for Matter at Silicon Labs, has worked on IoT devices, wireless protocols, and embedded devices for more than 15 years.

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