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When doing analog design, especially at higher frequencies ranging into the microwave region, it’s normal to focus on devices and the performance they enable in the specific topology. But there’s another aspect of microwave design that’s important to keep in mind: the role of advanced materials and the atomic-scale physics that allows conception, construction, and test of the advanced devices need to reach toward the multi-gigahertz part of the spectrum.

This is demonstrated by a recent Cornell University-led development related to voltage-tunable capacitors, or varactors, that combine high performance with low loss—and the road to get there. Traditional varactor technologies, while effective, often hit a performance ceiling due to intrinsic material limitations, particularly when it comes to dielectric losses that degrade signal quality.

A federal research program was initiated in 1999 to find materials for varactors that would offer lower dielectric losses at higher frequencies. The “back story” of success here is yet another example of how progress is often not linear, predictable, or obvious, despite the way it’s often portrayed.

The research team’s success here is due to persistence and following a very different path, as the project has been a long journey. While nearly every scientific team in the program focused on using barium strontium titanate, the Cornell team looked at layered crystalline materials, a type of perovskite structure known as Ruddlesden-Popper thin films, characterized by their exceptionally low energy loss at microwave frequencies.

Unfortunately, these films also had a major drawback: according to the accepted understanding of their crystal symmetry, they shouldn’t have been able to provide the tunability needed for practical devices.

A member of the research team was developing a new technique for measuring the dielectric properties of thin films across a wide range of frequencies. One of his measurements of strontium titanium oxide with composition Sr4Ti3O10, a layered Ruddlesden-Popper thin film, suggested something remarkable: the supposedly untunable material might, in fact, be tunable after all.

But there was a problem: the effect only appeared in an in-plane geometry, in which the electric field moved sideways through the material. Real-world devices such as voltage-tunable capacitors used in microwave circuits generally require an out-of-plane design, in which the electric field moves vertically through the film, enabling smaller, more efficient components.

Researchers spent a decade trying to find a way to preserve their low microwave loss while making them more tunable and more practical. They then asked a more radical question: what if they could change the symmetry of the material itself? If so, it might be possible to change the symmetry in a specific family of Ruddlesden-Popper compounds made from barium, strontium, titanium, and oxygen.

In a true multi-institution effort with collaborators at Cornell, the University of Connecticut, Rice University, the University of Maryland, Boise State University and the National Institute of Standards and Technology (NIST), they engineered a new version of the material by inserting carefully spaced rock-salt layers. The strategy effectively rewrote the material’s internal rules, allowing it to exhibit the out-of-plane behavior needed for practical devices while preserving the low-loss characteristics that had made the Ruddlesden-Popper thin films attractive in the first place.

By engineering a film structure that introduces a unique rock-salt atomic layer interleaved with every “n” perovskite unit cell, the researchers created a new class of thin films whose symmetry properties could be precisely controlled (Figure 1).

Figure 1 Researchers used advanced microscopy to confirm the atomic structure of an engineered Ruddlesden-Popper material. The diagrams show how alternating layers in the crystal helped produce the material’s unusual combination of tunability and low energy loss. Source: Cornell University

From possible breakthrough to despair, then to a solution

But this success led to another dead-end, as the new out-of-plane devices posed an entirely different metrology problem. The frequencies most relevant for modern communications systems are among the most difficult to measure accurately because at those high frequencies, the signal from the material can be distorted by the test structure itself—the metal electrodes, wiring, and geometries surrounding the dielectric. So, when the researchers first tested the new Ruddlesden-Popper devices at microwave frequencies, the results were confusing.

Addressing this issue, a NIST-based group began to develop a new metrology approach capable of characterizing the material in an out-of-plane, metal-insulator-metal capacitor geometry at frequencies beyond the reach of conventional techniques. They added a “control structure” using a sheet of metal that had the same topology as the device. Measuring that control structure let the team perform an additional round of calibration, subtracting away distortions caused by the test structure itself, and isolating the dielectric’s true microwave response (Figure 2).

Figure 2 The microwave measurement setup used by the NIST team in Boulder, Colorado. Source: NIST via Cornell University

Their custom-tailored composition exhibits a remarkable relative tunability of 51% under an applied electric field of 250 kV/cm, which is almost double the performance of many conventional tunable dielectrics. At the same time, it maintains an impressively low dielectric loss that translates to a material quality factor of about 200. For the best version, the measured dielectric tuning figure of merit (FOM) showed tenfold improvement for out-of-plane tunable dielectrics at 10 GHz.

Figure 3 Various perspectives on microwave characterization are displayed at ambient temperature. Source: Cornell University

Will this lead to new varactors that you can buy? Obviously, it’s too early to say; there are still many potential obstacles on the path to commercialization, if it even happens.

But I do think the right screenwriter could make an exciting story out of this long quest with its advances, insight, contrary thinking, roadblocks, and eventual success. It would be nice to see a true story of science discovery and innovation captured and brought to a more general audience (can you think of any recent ones other than the 2023 blockbuster movie Oppenheimer?).

The work is detailed in their intense paper with a deceptively simple title “Breaking symmetry yields a low-loss out-of-plane tunable microwave dielectric” published in Nature Electronics; while that paper is behind a paywall, a “student” preprint copy is posted at ResearchGate here. In addition, there’s a fairly technical yet very readable description of the work posted at Bioengineer.org (why there—I can’t say).

Bill Schweber is a degreed senior EE who has written three textbooks, hundreds of technical articles, opinion columns, and product features. Prior to becoming an author and editor, he spent his entire hands-on career on the analog side by working on power supplies, sensors, signal conditioning, and wired and wireless communication links. His work experience includes many years at Analog Devices in applications and marketing.

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The post Deep physics, materials science enhance dielectrics, varactors appeared first on EDN.

Сторінки життя Містера Лазера. До 85-річчя від дня народження засновника української лазерної технології Володимира Коваленка Інформація КП ср, 07/15/2026 - 17:00

Ascent Solar Technologies Inc of Thornton, CO, USA – which designs and makes lightweight, flexible copper indium gallium diselenide (CIGS) thin-film photovoltaic (PV) panels that can be integrated into consumer products, off-grid applications and aerospace applications – has recapped its commercial progress and solar material development in first-half 2026, and outlined its objectives to spur growth and advancement throughout the second half of the year...

➡️ Реєстрація на відбір до ветеранської магістерської програми за спеціальностями «Прикладна механіка» та «Біомедична інженерія» kpi ср, 07/15/2026 - 15:11

The iconic LM2917 tackles frequency-to-current conversion with (very) few externals.

Almost exactly 50 years ago—in June 1976, to be precise—National Semiconductor introduced the LM29x7 series, offering deceptively simple monolithic solutions to a frequently encountered signal processing problem: the flexible and accurate conversion of frequency into an analog signal.  I say “deceptively simple” because actually, these chips are very capable interfaces with versatile inputs, internal active zener voltage references (with the LM2917), and a configurable output that includes an opamp-driven uncommitted Darlington transistor.

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

Although initially targeted at automotive applications, the LM29x7 series’ flexibility makes them highly handy in other contexts, including industrial applications like monitoring turbine-type flow meter flow rate and small motor tachometry. Figure 1’s facile conversion of a frequency input to a universal 4-20mA current loop format shows how minimalist—it makes do with just nine paltry passives—such a circuit can be when implemented with a LM2917.

Figure 1 A 2917 with internal voltage reference converts a 0-5kHz input to a 4-20mA output.  Single-pass calibration of both ends of the output span is available. First step: input 0Hz and adjust R1 for 4mA output. Second step: input 5kHz and adjust R2 for 20mA. Third step: there is no third step. You’re done.

Here’s how it works.

Incoming pulses are converted by the internal Schmidt trigger comparator and charge pump into constant-current (180uA) pulses delivered to pin 3. Each pulse cycle carries a charge quantum Qp = VzC1 so that the average current out of pin 3 as a function of the Finput frequency is I3 = Fin Qp = Fin Vz C1. For the values shown, that works out to I3 = 7.56uA/kHz = 0 to 38uA as Fin goes from 0 to 5kHz. For calibration stability, C1 should be a temperature-stable type like C0G.

The R1…R4 resistor network hung from pin 3 converts this 0 to 38uA to 0 to 4v which is added to a 1v offset supplied by R3. The resulting 1 to 5v total is converted by the internal output opamp and Darlington via current sense R6 to the final 4 to 20mA output. R7 provides some bias current cancellation, which is useful since the thirsty opamp inputs can draw as much a 500nA. If uncorrected, that could create a 50mV voltage offset error on pin 3. Meanwhile, C2 provides ripple-suppression filtering.

However, none of this explains why R1 and R2 are variable. Here’s why. Although U1’s spec’d linearity and temperature coefficient are good, its initial tolerances aren’t so great: about +/-10%.  See “gain constant K” in Table 7.5 here (PDF). Therefore some post-assembly final calibration is pretty much unavoidable, which is the purpose of R1’s (4mA zero) and R2’s (20mA full-scale 5kHz) tweakability.  But at least if you do the adjustments in the right order (first R1, then R2), they won’t interact and calibration can be completed in s single pass.

So it shouldn’t Hz too much. (No such promises for his jokes, however! Ed.)

Stephen Woodward‘s relationship with EDN’s DI column goes back quite a long way. Over 200 submissions have been accepted since his first contribution back in 1974.  They have included best Design Idea of the year in 1974 and 2001.

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The post Painlessly convert Hz to 4-20mA current loop appeared first on EDN.

Deploying AI inference in powerconstrained and missioncritical environments such
as aerospace and defense systems requires solutions that balance performance, efficiency, reliability and
ease of development. To better manage these challenges, Microchip Technology (Nasdaq: MCHP) has
released the VectorBlox 3.0 Accelerator Software Development Kit (SDK) to help simplify FPGAbased AI
implementation and speed timetomarket. Offered to developers free of charge, VectorBlox 3.0 SDK and
associated CoreVectorBlox IP is designed as an integrated toolchain that streamlines optimization,
compilation and deployment of convolutional neural network (CNN) models on PolarFire FPGA and SoC-
based platforms. Because the accelerator scales efficiently across model sizes and supports multiple AI
workloads on a single device, customers can consolidate various vision or sensorbased AI functions on a
single low power FPGA.

“As AI models continue to grow in complexity, compression is becoming essential for deploying intelligence
at the edge,” said Shakeel Peera, corporate vice president and GM of Microchip’s FPGA business unit.
“With VectorBlox 3.0, we’re leveraging sparsity-based model compression from our Neuronix acquisition to
reduce compute demands while preserving accuracy.”

With support for sparse neural networks, VectorBlox 3.0 helps enable efficient execution of vision-based
CNN models by skipping zerovalued operations. This capability helps developers accelerate inference
performance while reducing power consumption, an important advantage for alwayson edge AI
applications that must balance responsiveness with energy efficiency. Enabling sparsity-based model
compression is designed to reduce compute and memory demands, while preserving accuracy.

“Leveraging VectorBlox acceleration on Microchip’s PolarFire SoC enabled us to efficiently deploy advanced
onboard AI pipelines for low-latency payload operations in orbit,” said Vito Fortunato, SPACEDGE
services line manager at Planetek Italia. “The platform allowed us to validate real-time Earth Observation
processing capabilities including object detection, semantic scene analysis and edge-generated actionable
information products on top of the AI-eXpress-1 satellite, deployed in 2025, while providing the radiation
resilience and operational reliability required for continuous Low Earth Orbit operations.”

Additionally, Spacecraft Pose Network v2 (SPNv2), a neural network designed to estimate position and
orientation using vision data, enables autonomous navigation and proximity operations in space for
applications such as autonomous rendezvous and docking, space debris removal, satellite inspection and
formation flying. Built on mid-range, power-efficient, single-event-upset (SEU) immune PolarFire FPGAs and
SoCs, the solution delivers secure boot, anti-tamper protection and high reliability for harsh environments.
These features are necessary for missioncritical defense, aerospace and industrial deployments where long
operational life, data protection and system resilience are essential.

“The combination of PolarFire SoC and VectorBlox creates a powerful synergy for deploying AI-powered
autonomy solutions directly in orbit,” said Federico Fontana, Head of Hardware Engineering at AIKO. “We
validated this through the deployment of our clear_CHARLES suite, which provides onboard cloud and ship
detection for adaptive and autonomous payload operations on power-efficient platforms, making a further
step toward increasingly autonomous, responsive and software-defined space systems.”

The post Microchip Advances Neural Network Implementation with VectorBlox 3.0 Accelerator SDK appeared first on ELE Times.

Efficient Power Conversion Corp (EPC) of El Segundo, CA, USA — which makes enhancement-mode gallium nitride on silicon (eGaN) power field-effect transistors (FETs) and integrated circuits for power management applications — is participating in the Tech Taipei Power 2026 power electronics conference in Taiwan, which gathers industry experts to discuss the latest advances in power conversion technologies for AI infrastructure, industrial systems, automotive, robotics, and next-generation energy applications...

Aehr Test Systems of Fremont, CA, USA — which provides solutions for testing, burning-in and stabilizing semiconductor devices in wafer-level, singulated die, and packaged-part form — has received more than $8m in new silicon carbide wafer-level burn-in (WLBI) orders...

The governments and industry partners of Australia, Japan and the USA, together with Alcoa Corp (which provides bauxite, alumina and aluminium products), have reached a final investment decision for a gallium production plant to be co‑located at Alcoa’s Wagerup alumina refinery in Western Australia...

Are you an engineer contemplating your next “skillset” move in the AI era? If so, the ‘Mind of the Engineer’ survey is for you. The survey delves into multiple engineering disciplines, the latest technology trends, and emerging design skillsets to formulate empirical observations about where today’s engineering landscape is heading and how engineers should prepare for this AI-powered paradigm shift.

More importantly, this survey touches a hot nerve: AI’s potential to eliminate engineering jobs. Will AI fundamentally change what it means to be an engineer in the next five years? And do engineers trust AI-generated outputs, and do these outputs reflect biases in training data?

The survey also tests grounds for young engineers, where AI and machine learning (ML) skills are hot favorites. Will these disciplines take over computer science and engineering? Should engineers go for self-study efforts through books, papers, blogs, and YouTube videos, or should they opt for education courses or certificates with Coursera, edX, Udemy, and IEEE?

The survey also attempts to gauge where engineers stand in terms of effectively using AI tools in electronics design and manufacturing processes. That includes agentic AI, formal AI, AI certifications, LLMs, and AI-assisted EDA tools. Also, how comfortable engineers are in AI/ML model development and deployment.

The survey also digs deeper into how engineers are using chatbots/assistants such as ChatGPT, Claude, Gemini, and Copilot Chat. Then there are AI coding assistants like GitHub Copilot, Cursor, and Tabnine. The survey attempts to establish where these tools stand in an engineer’s day-to-day work and what the actual productivity gains are.

That brings us to a sensitive and crucial issue: Will AI tools eliminate more engineering jobs than they create? Are AI tools making engineers significantly more productive overall? Will AI fundamentally change what it means to be an engineer in the coming years?

However, the survey isn’t all about AI; cybersecurity and quantum computing are presented as pressing issues on many engineers’ minds. For instance, where does quantum computing stand in its deployment timeline? And how aware engineers are in terms of quantum-safe products and post-quantum cryptography.

Next, the survey covers quickly emerging technologies such as chiplets and advanced packaging. Likewise, power electronics stars—silicon carbide (SiC) and gallium nitride (GaN)—are also there. Not to be discounted, edge AI, a rapidly emerging offshoot of AI technology, is there as well.

AspenCore, publisher of EDN, is conducting the “Voice of the Engineer” survey. Once you complete this survey, you will become eligible to be randomly selected as one of 10 respondents to receive an Amazon.com Gift Card valued at USD 100 (or local equivalent). Results of this survey will be announced at a major industry event with a fanfare. They will also be posted on EE Times, EDN’s sister publication.

Participate in the survey and be part of this timely engineering conversation about the future of AI and the electronics industry at large.

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The post ‘Mind of the Engineer’ survey: A reality check on where EEs stand on AI appeared first on EDN.

Indian deep-tech startup Meine Electric has developed the world’s first fast-charging iron-air battery system, marking a significant technical advancement for long-duration energy storage (LDES). The technology, which features a proprietary Fast Charge Long Discharge (FCLD) capability allowing the battery to charge in 6 hours and discharge over 18 hours, has been independently validated by Customized Energy Solutions (CES), a US-headquartered global energy services and technology company and the parent organization of the India Energy Storage Alliance (IESA).

The independent testing was conducted by the CES Battery Laboratory through a structured testing protocol designed to assess electrochemical performance, capacity retention, and operational stability. Testing two iron-air electrochemical cells, the assessment validated the system’s operational stability under an asymmetric duty cycle comprising a rapid 6 hours of charging followed by 18 hours of discharging per cycle.

Historically, iron-air battery chemistry has been limited by longer charge cycles, with existing approaches focused on multi-day storage applications. According to DataM Intelligence’s Iron Air Battery Market Size, Long-Duration Energy Storage Forecast 2035; 2026 Report, Iron-Air batteries are emerging as a promising solution for long-duration energy storage due to their ability to provide reliable and scalable storage using abundant materials such as iron. Global players, including Form Energy (US) and Ore Energy (Europe), have built Iron-Air systems focused on long-duration applications, with existing approaches demonstrating storage durations of up to 100 hours. However, these longer cycle times have restricted its suitability for daily renewable energy balancing.

Meine Electric has addressed this limitation through its proprietary Fast Charge Long Discharge (FCLD) technology, electrode development processes, and new system architecture. The technology enables a 6-hour charge and 18-hour discharge cycle, making it the world’s first fast-charging Iron-Air battery technology designed for daily-cycling renewable energy applications. The chemistry also offers fundamental advantages including inherent safety, reliance on abundant raw materials, and lower system costs compared to lithium-ion.

“Traditional iron-air systems are restricted to multi-day cycles, making them largely incompatible with the intermittent nature of renewable energy generation. Furthermore, solar-heavy grids don’t need a 100-hour battery. Unlike regions that require seasonal storage, the renewable challenge across Asia and MEA is fundamentally a daily balancing problem. By engineering our system to capture a full charge within a tight 6-to-8-hour window, we are finally turning iron-air batteries from a sluggish backup chemistry into a foundational, daily-cycling asset,” said Priyansh Mohan, Co-founder and CEO of Meine Electric.

This daily-cycling capability aligns precisely with the requirements of rapidly growing renewable energy markets like India, where the grid demands storage technologies that can consistently capture excess solar generation during the day and discharge it reliably overnight. For Meine Electric, it reinforces the company’s progress from laboratory-scale development towards real-world deployment of next-generation storage solutions.

Founded in 2023 by Priyansh Mohan and Stuti Kakkar, Meine Electric is the first company in APAC, and the third player globally, pioneering iron-air long-duration energy storage. As India races towards its 500 GW renewable energy target by 2030, Meine Electric is positioned to deliver key strategic advantages:

• Market-Leading Costs: The proprietary technology operates at a levelised cost of storage (LCOS) of less than $0.05/kWh (~₹5/kWh).
• Daily-Cycling Capability: Affordable, daily-cycling batteries that seamlessly complement lithium-ion to unlock true round-the-clock renewable power.
• Grid Infrastructure: Systems capable of functioning as foundational assets to firm renewables and flexibilize thermal assets.

“The energy industry prioritizes lower costs and higher reliability, and iron-air technology is structurally positioned to deliver on both. We are betting on iron-air as a foundational infrastructure play to support the renewable grid,” stated Stuti Kakkar, Co-founder and COO of Meine Electric.

The validation by CES adds critical third-party credibility to Meine Electric’s technology roadmap as the company moves towards larger-scale demonstrations, pilot deployments, and continues developing indigenous energy storage solutions to secure India’s transition to renewable energy.

The post Indian Deep-Tech Startup Meine Electric’s Fast-Charging Iron-Air Battery Clears Independent Validation, Paving the Way for 24/7 Renewable Energy Storage appeared first on ELE Times.

"This model was specifically designed for microelectronics, eliminating the image lag common in cheap USB microscopes connected to a PC." submitted by /u/dataclone82 [link] [comments]

Here's some animations I made of transistors turning on and off. In order, we have an NPN BJT, n-channel MOSFET, and finally an n-channel JFET. The red and blue dots represent electrons and holes, and white flashes are recombination events. The density of dots is proportional to the actual density of charge carriers. In the first set of animations, the velocity of the dots is equal to the velocity obtained by summing the diffusion and drift currents and dividing by the charge density, but diffusion is not explicitly shown. In the second set of animations, the dots undergo diffusion and drift, and this makes it a more correct depiction of carrier motion. The drawback is of course that the jiggling makes it more visually confusing. I made these with my semiconductor simulator (https://brandonli.net/semisim/). I also have higher quality versions of the animations here. submitted by /u/thepowderguy [link] [comments]

Global smartphone shipments fell 4% year-on-year in second-quarter 2026 as the ongoing memory crisis disrupted supply and pushed up component costs, according to Omdia’s Smartphone Horizon Service for July 2026 (sell-in shipments). The current dynamic has created severe market polarization, reflecting stark differences in vendors’ mitigation strategies, which vary according to their priorities, scale, price-band focus, and core audience demographics, notes the market research firm...

submitted by /u/brolpe [link] [comments]

If you periodically need to see the frequency response of a circuit, this easy, inexpensive project can help you out.

Editor’s note: This is a two-part series on how to create a noise generator with an adjustable bandwidth and a consistent amplitude. The previous entry: 

• Making noise with a BANG, part 1: Concept and hardware

The operation and firmware

As I mentioned last time, I was able to reuse much of the firmware from a previous Design Idea project. The Arduino C code consists of three files. One is the initialization code for the DAC, while another contains code for the LCD/touch screen operations. The third is the main code. Let’s look at these one at a time.

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

The DAC initialization code does just what it says and is designed to get a DAC output as fast as possible. The LCD/touch screen code is the largest piece of the software puzzle. Before discussing it “under the hood”, let’s take a quick look at the some of the LCD/touch screen display outputs. Figure 1 shows most of the screens used in the BANG.

Figure 1 The BANG LCD screens are designed to be both intuitive and informative.

The first screen you see after the power-up splash screen is what I call the main screen. It allows you to select an output, but let’s hold off discussing this implementation aspect in detail until later. For now, just understand that on power-up, it will default to the noise output on the AC and DC BNC connectors.

Also on the main screen is the “Change Bandwidth” selection that will allow you to set the bandwidth for the noise (noise bandwidth is measured from 0 Hz). When you press “Change Bandwidth”, the screen will change to the keypad and allow you to enter your desired number. Note that if you exceed the maximum 225 kHz it will default to 225 kHz. Similarly, if you enter a number less than the minimum of 500 Hz it will default to 500 Hz. After hitting “ENTER” you will return to the main screen.

On the main screen, selecting “About” will take you to a screen showing lots of interesting information such as your selected bandwidth and the gain it will apply to the noise during filtering. You’ll also see the sample rate (which is fixed), firmware version, and (for those that are interested) your current IIR filter’s coefficients. Next, it shows the battery voltage and charge level. (If you do not have a battery installed you may see fully charged numbers as it is instead reporting the charger voltage. There is a #define in the top portion of the main code that you can set to “false” instead, in which case this line won’t be displayed if you don’t have a battery installed.) The last item shown is the incoming USB voltage.

The last screen shown in Figure 1 is the one displayed when “RUN” is selected on the main screen. If you see this screen, the noise signal is being generated and is being output to the BNC connectors.

Let’s talk a little about the code for creating these screens. It’s a bit long and mostly involves setting colors, drawing boxes, selecting fonts, aligning text in the box, and capturing positions of key presses. Almost all of this is done using higher level calls to the downloadable “Adafruit GFX Graphics Library”. Here’s a short example of the code showing how to display the word “BANG” in red against a grey background:

tft.fillScreen(tft.color565(0xe0, 0xe0, 0xe0)); // Grey tft.setFont(&FreeSansBoldOblique50pt7b); tft.setTextColor(ILI9341_RED); tft.setTextSize(1); tft.setCursor(13, 100); tft.print("BANG");

The third C file is the main code, which mostly directs calls to the correct LCD screen, executes miscellaneous housekeeping operations, and (of course) generates the noise signal, the latter starting with the bandwidth selected from the touchscreen. Using this value, we generate the coefficients for a digital 2-pole low-pass Butterworth IIR filter. The next step is to get a value for the gain we will be using on the noise signal. This is done by calling a function that has the bandwidth as an input and returns a gain number. Here is the code for that function:

//****************************************************** // AGC * // Does an automatic gain adjust to the * // random number amplitude. Run once after * // startup or a change in the LP filter. * //****************************************************** float AGC(float cutoff_freq) { float agcGain = 1; // Calculate agc gain based on the set bandwidth if (cutoff_freq >= 50000) agcGain = 31.0 * pow(cutoff_freq, -0.292); // for 225kHz to 50kHz else agcGain = 393.769851 * pow((cutoff_freq - 97.8961702), -0.524598029); // Curve fit of freq vs. amplitude data gainOffset = 1024.0f * (2.0f - agcGain); // Adjustment for shift in DC level return agcGain; }

You’ll see that there are two different formulas used for agcGain, based on whether the bandwidth selected is greater than 50 kHz. This dual-equation method makes curve fitting more accurate. These formulas were derived from data I generated by setting a bandwidth and then adjusting the gain in code to get a desired amplitude. The data was then used to generate curve-fitted equations (kudos to Standards Applied Engineering Tools, whose Curve Fitting Online utility gave by far the most accurate curve fit of all the tools I found and tried). Later, I’ll also detail how AI did (or, maybe more accurately, didn’t) with generating the same curve fit equation(s).

You can see from the second equation that the power function is based on -0.52; roughly the square root of 2 as we talked about at the beginning of part 1 of this series. The reason it is not exactly a square root of 2 function is because some noise, beyond the cutoff frequency of the 2-pole digital IIR filter, still exists in this roll-off portion of the filtered signal – i.e., it is not a brick wall filter.

Figure 2 shows a graph of this gain vs. bandwidth selected.

Figure 2 This graph shows the linear gain vs. bandwidth result for the equations used in this design.

With the bandwidth entered and the gain calculated, it is then incorporated into the coefficients of the lowpass IIR filter. This approach optimizes the calculations; we don’t need to add another multiplier inside the speed-optimized output loop.

Ok: we’re now ready to generate the noise signal. When the user selects “RUN”, the code enters a tight loop. In it, we get a random number from the true random number generator (TRNG). Next, we run the number through the IIR filter, which also applies the gain. Then, the lower 12 bits of this number are sent out of the DAC. (A note: the DAC has a slew rate of somewhere around 1 µS per volt to minimize the effect. The number is scaled to keep the signal mean coming from the DAC to around 1/2 Vcc.) This loop continues until the user selects “STOP”.

Those of you following closely may be thinking something along the lines of the following right now: “Another way to generate a noise signal of a given amplitude is to simply generate the random samples at a lower sample rate”. The downside of this alternative approach is that the analog reconstruction filter would need to be adjusted to follow the sample rate, which seems like a much more difficult analog design task. Also, we would still need to perform the digital low-pass filtering for anti-aliasing.

It’s time to look at the output of the BANG. Figure 3’s scope display shows the AC output time domain signal on the left and the FFT on the right. The BANG is set to give an output with a 25 kHz bandwidth.

Figure 3 This scope plot shows the BANG output with a 25 kHz bandwidth setting.

The enclosure

The BANG’s enclosure derives from a custom 3D-printable model (see later for a file-download link). It includes three parts: the main body, the base/PCB mount, and a stylus for the touchscreen. The main body’s download is modeled with two filament colors but can alternatively be printed in one color. If printed in a single color, the text is still readable, as it is also embossed. The base holds a 120 mm x 80 mm PCB. I used a protoboard as there were a minimal number of parts and was faster to build than designing and waiting for a custom PCB.

Wait, there’s more

While TRNGs are common in larger processors, they’re more rare in smaller micros. Most compilers therefore use pseudo-random number generators instead. But since this system was generating 32-bit true random numbers, it occurred to me that such a data stream may also have other uses, such as in cryptography systems, input data for testing code, a “seed” for pseudo-random number generators, or even helping you select “picks” for playing the lottery.

More broadly, it seemed like a waste to not have a way to output these generated numbers. So, I included support for this feature, via USB, in two format options – ASCII data or binary data. The desired format can be chosen from the “Select Output” LCD page shown in Figure 4 (as mentioned earlier, the power-up default is the noise generator output via the analog BNC connectors).

Figure 4 The design includes support for outputting the 32-bit true random numbers generated, over USB and in two format options.

Note that although the data is 32 bits, it can be sliced or appended to form any size random number you require. For example, you can use one bit of the 32-bit source, which will still be random, or you can append two 32-bit output numbers to create a truly random 64-bit number.

Comments on AI use

I only used AI (and then only experimentally) for one part of the project, the curve fitting of test data to create the equation(s) for the AGC. The result was…interesting. I’d already developed the earlier discussed frequency-to-gain equations for the AGC algorithm, but I thought I should also try AI to see what it came up with. I fired up Microsoft Copilot and gave it the frequency vs gain data that I’d already created by iteratively setting a frequency and then adjusting gain in the code until I got the fixed amplitude I was looking for.

Copilot noted that it looked like a power equation – good. Then it gave me a very simple equation: gain = 1.96 * freq-0.52 . Wow, I thought, much simpler than the equations I’d came up with. But it seemed too good to be true, so I got out a calculator. At a frequency of 10 kHz the gain should be around 3. When you make the calculation on the AI’s formula you get around 0.016. When I asked Copilot to use its equation on 10 kHz, it said the gain would be 3.68. Another AI with a case of cognitive dissonance. Perhaps obviously, I used the other formula instead!

Conclusion

This is a fairly easy and inexpensive project to build. If you periodically have the need to see the frequency response of a circuit, it may help you out.

Note that the schematic, code, 3D print files, Arduino software, links related to various parts of the project, and additional notes and pictures on the project’s design and construction can be downloaded for free at the MakerWorld website.

Damian Bonicatto is a consulting engineer with decades of experience in embedded hardware, firmware, and system design. He holds over 30 patents.

Phoenix Bonicatto is a freelance writer.

Related Content

• Making noise with a BANG, part 1: Concept and hardware
• A digital filter system (DFS), Part 1
• A digital filter system (DFS), Part 2

The post Making noise with a BANG, part 2: Software, integration and operating results appeared first on EDN.

Very simple circuit - i had a bunch of these charger/booster modules laying around and decided to learn about the dangers of bare 18650 batteries. Excited to do more. submitted by /u/Willy_V3 [link] [comments]

I wanted to modify my PS3 controller to USB-C, but it used a through hole connector. Not a problem! submitted by /u/circuitsable [link] [comments]

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