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In conjunction with announcing on 28 October that they have agreed to merge in a cash-and-stock transaction (valuing the combined firm at $22bn) that is expected to be finalized in early 2027, both Skyworks Solutions and Qorvo have reported preliminary September-quarter financial results exceeding their respective guidance...

"Промінь" Наталії Гмирі у Центрі культури та мистецтв Інформація КП ср, 10/29/2025 - 18:01

For the past couple of years, I’ve been using AI to assist in the design of my hardware and firmware projects. The experience has generally been good, even though the outcome isn’t always useful. So, I’m presenting a short summary of a few of the tasks I have attempted and providing my unscientific grade to the outcome. The grades will be averaged at the end. Note: I do not have any paid AI subscriptions—I only used free AI tools, mostly Microsoft Copilot and ChatGPT (although I have tried a few others). These are just a few of my experiences using online AI.

Do you have a memorable experience solving an engineering problem at work or in your spare time? Tell us your Tale

Converting voltage to percent charge

Grade: A

I wanted to show the charge remaining in a lithium polymer battery used to power a design. This is not straightforward as the function to convert voltage to percent charge for a lithium polymer battery is not a linear function. I asked Copilot to make a table of 20 voltages from 3.2 V to 4.2 V and their respective charge percentages. Then I asked it to create a C function to do this conversion. It created this nicely, including linear interpolation.

Finding the median without sorting

Grade: D

A while back, I wrote a Design Idea (DI) article on non-linear filters. While doing this, I queried Copilot to create a C program that can find the median of 5 numbers and do this without sorting. (No sorting for a small number of points is useful for increasing speed.) It created a nice-looking program—nice formatting and good comments. It also compiled fine. The problem was that the program didn’t work—it found the wrong value for the median in some cases.

Initializing an ADC

Grade: C+

Another project required me to write code for the SAMD51 MCU to initialize the ADC for high-speed sampling. As I was trying to get maximum speed from the ADC, it was a somewhat complex setup, especially the clocking system. I tried creating the code in both Copilot and ChatGPT multiple times.

Some code would not compile due to things like bad register names, and some code would just not work, giving no ADC readings. After some back and forth, those issues were corrected. A few of the comments in code were misleading or just plain wrong as it applied to clock frequencies. As the code got close to a working function, I took over the code and reworked parts of it to make it work.

Graphic design

Grade: C+

I was doing some LCD graphics design for a project, and one part was a battery charge indicator. This symbol, for battery percent of charge, was to be displayed on an LCD with an ILI9321 controller. (This standard figure looks like an AA battery with a green interior representing the percent charge.)

I asked Copilot to write C code for this using the GFX graphics library. The length of the green fill worked well, but the battery figure looked nothing like a battery. It was a rectangle with two large circles on both ends. I had to rewrite portions of the code myself.

Grade: F

In the same project, I asked Copilot for a USB symbol written using the GFX graphics library, as above. This didn’t look like the trident-like, universal USB symbol. I was essentially three sticking out from a central point at various angles. It was unusable.

Enclosure design

Grade: D-

Next, I tried to have Copilot and ChatGPT design an enclosure that would work on a workbench, allowing the user to see the LCD and to easily connect BNC cables. All I got were images of rectangular boxes. No matter how I asked for a more unique shape, it never went much beyond a rectangular enclosure. Then, even the rectangular box could not be delivered as a usable 3D file “step”, “stl” file without using other programs.

Filter design

Grade: C-

I asked ChatGPT, “Can you design and display a circuit that takes in a signal, AC couples it to a gain stage of 5, and then filters it at 120 kHz before outputting it?” Instead of explaining the result, the image in Figure 1 will speak for itself.

Figure 1 ChatGPT’s output for a filter design that takes in a signal, AC couples it to a gain stage of 5, and then filters it at 120 kHz before outputting it.

It did include a nice explanation of how components were selected, but the schematic was mostly unreadable. Dedicated tools such as TI’s Webench filter design tool, Analog Devices’ Filter Wizard, and ST’s eDesign Suite are the right tools for filter circuit design and are actually easier to use.

Grade: Ungraded

I tried to create C code, in both Copilot and ChatGPT, for calculating coefficients for a digital Sallen-Key 2-pole high-pass, low-pass, band-pass, and band-stop filters. I tried many times and could not get a good working algorithm. The code was close, but the filters did not function correctly. Eventually, I found the code after an extensive Google search. It’s possible my testing may have been part of the problem—unsure.

Grade: B

Along the way, I tried lots of smaller queries, many of which were very helpful.

A lab notebook

I’m sure some of the issues are my skill in creating the AI prompts. This certainly made my attempts take longer as I had to add more detail in follow-up prompts. I actually found this conversational style more engaging than using search engines. It’s not like a Google search, where you can’t typically do follow-ups to your query—you have to re-enter your original query with a modification.

The AI systems work much more like a conversation with a colleague. You can tell it that the code it gave you did not compile, as it didn’t recognize a register name. Or you can ask it to give you faster code, or change a resistor value in a circuit, and recalculate the remaining components.

One thing I learned when writing this article is that both ChatGPT and Copilot keep a complete history of conversations we had. It’s sort of like a lab notebook, showing your path to a certain design—very helpful.

A C rating

Looking at the average grade, it comes in between a C and a C-. I’ll give it the benefit of the doubt and call it a C. The C rating matches my gut feel also. The interaction is fairly easy—it feels like interacting with a coworker. The conversation goes on, attempting to fine-tune the final answer. The interaction process is much better than doing a Google search and getting a list of things to pick from, without an easy way to refine the search.

Does it save time? That’s hard to judge as I’m still learning how to create better prompts. Sometimes I get a useful answer right away. On more complex queries, I’ve gotten pulled down a rabbit hole and wasted time while the solution diverged from what I was looking for. There have been times when it had me trying to finetune the result, and I turned to Google and got an answer much faster.

You can easily be lulled into the feeling that you’re conversing with a savant, but it may be more like AI-splaining. Every answer exudes confidence, but it could be the confidence of ignorance. Remember that these answers have not been checked or tested.

Will I continue to use it? Certainly… I’ll get better at using it, and the tools will continue to improve. What I would like to see is an AI tool focused specifically on electrical engineering (hardware, firmware, and system design). This may focus its skills on finding or creating circuits, and being able to dig down deep into data sheets, etc. It would also be nice if it could test its results through simulation or by executing the code in a series of tests. Maybe in the future.

All in all, it’s worth using and everyone should give it a try, just check the answer closely.

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.

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The post How help are free AI tools for electronic design? appeared first on EDN.

Many industrial applications in the automotive, automation, appliance, or medical sectors require power supplies that comply with functional safety standards. If the input voltage of such a power supply is not within its specification, the system to which it is supplying power is potentially operating in an unsafe state. Monitoring input and output voltages for faults such as undervoltage, overvoltage, and overtemperature may require resetting and transitioning the system to a safe state.

Defining the protections needed to comply with functional safety standards depends on the safety level, which the design engineer must determine in cooperation with a safety inspection agency such as Technischer Überwachungsverein. The engineer must also work on a time-consuming risk assessment of failures that address both safe and dangerous failures as well as random and systematic failures.

Functional safety in power supplies

Safety standards such as IEC 61508 or ISO 13849A specify the maximum allowable probability of dangerous failures per hour.

The requirements for a safe power supply as specified in IEC 61508, which covers functional safety in industrial manufacturing, include overvoltage protection with safety shutoff, secondary-side voltage control with safety shutoff, and power-down with safety shutoff. These protections require significant additional external circuitry around the switched-mode power supply (SMPS).

A safe power supply must also fulfill random hardware fault requirements. Using an integrated PG pin as the safety mechanism to monitor failures can be insufficient, because this pin is typically not independent; it shares the same internal band gap with all safety and monitoring features. A drifting band gap will cause the PG pin to fail. This is known as a common-cause failure, which does not meet functional safety requirements.

As shown in Figure 1, detecting any fault will also require additional supply-voltage supervisors as well as a switch connected in series to the input; alternatively, the switch could connect to the output. This switch disconnects the system from the source or load in case of a failure. Redundant supply-voltage supervisors monitor the input and output voltages. Typically, an industrial power supply is limited to less than a 60-VDC input, even in the event of a fault, requiring an additional circuit with transient voltage suppression and a fuse, because not all devices are specified to 60 V.

Figure 1 An industrial safe power supply example block diagram. Source: Texas Instruments

The switch at the input, which is under the control of the monitor, can remove power in case of a failure. The input and output voltage are monitored continuously. As I mentioned earlier, to comply with functional safety standards, all parts must operate within a specified operating voltage. That is not an easy task, given the requirement to detect undervoltage and overvoltage events immediately.

Buck converter

Using a functional-safety-compliant buck converter with integrated safety features can greatly reduce the amount of external circuitry, as shown in Figure 2. An integrated redundant circuit, which replaces the external voltage supervisor, has a startup diagnostic check and can detect the failure of a FET. This implementation reduces the overall cost of designing a safe power supply.

Figure 2 Integrated functional safety features replace an external voltage supervisor, reducing circuit complexity. Source: Texas Instruments

The nFAULT pin in the converter is used for overvoltage protection and as a failure flag. Triggering the nFAULT pin disables a safety switch, which in this case is an ideal diode controller connected to the input. The Temp pin communicates the temperature to a microprocessor and forces a shutdown if the temperature is too high. The VSNS pin has feedback path failure detection, and there is another feedback divider for redundancy. During startup, the LM68645-Q1 buck converter checks the configuration on the RT, FB, and VSNS pins.

Figure 3 shows a block diagram of a universal board (configurable to meet different safety standards)—with an input voltage range of 19.2 V to 28.8 V and a maximum 60 V—for a safe power supply.

A synchronous buck converter generates a 5-V output with a maximum current of 3 A. Beside the buck converter is an ideal diode with back-to-back MOSFETs connected to the input. An ideal diode connects to the output. The nFAULT pin can control both switches. Two additional supervisors for redundant voltage monitoring on the input and output can disable both switches as well. The ideal diode controller has power-path control and overvoltage protection. The voltage supervisors also provide built-in self-test and overvoltage and undervoltage protection.

Figure 3 The TI Industrial 24 V to 5 V safe power supply reference design, where a number of redundant options on the board make it possible to comply with different functional safety standards. Source: Texas Instruments

A buck converter designed to help meet functional safety standards reduces the amount of necessary functional safety documentation, system cost, and time to market. Because all of the devices in the 24 V to 5 V safe power supply reference design are specified for ≥ 60 V, an input transient voltage suppressor or fuse is not necessary.

Upgrading a safe power supply

Although upgrading a safe power supply to a higher standard requires significant effort, it is possible to design a power supply that meets functional safety requirements but also decreases time to market and system cost. Using a buck converter with integrated safety features helps achieve systematic and random hardware metrics and reduces the needed external circuitry.

Florian Mueller, systems applications engineer, Texas Instruments

Related Content

• Attaining functional safety: Managing random failures
• Motor control for functional safety
• Enabling functional safety in automotive processors
• Designing power supplies for industrial functional safety, Part 1
• Approaches to functional safety in automotive design

 

 

The post Power Tips #146: Design functional safety power supplies with reduced complexity appeared first on EDN.

CEO Gervais Jacques to be appointed executive chairman in May, as Luc Bertrand becomes lead independent director...

Infineon Technologies AG of Munich, Germany is launching EasyPACK C, the next generation of its EasyPACK package family, for industrial applications such as fast DC electric vehicle (EV) charging, megawatt charging, energy storage systems and uninterruptible power supplies (UPS) operating under harsh conditions and fluctuating load profiles, which demand high efficiency, robust power cycling capability and long lifetime...

⭐ Стажування у ГО «Центр логістики розвитку громад»! kpi ср, 10/29/2025 - 14:36

📣 2026 IEEE 8th International Conference on Smart Technologies in Power Engineering and Electronics (STEE) kpi ср, 10/29/2025 - 13:18

The power gallium nitride (GaN) device market is rising at a compound annual growth rate (CAGR) of 42% from $355m in 2024 then growing sixfold from now to about $3bn by 2030, forecasts Yole Group in its report ‘Power GaN 2025’...

The post The Sole and Original Show Focused on Core Technologies Pertaining to the Automotive Industry – Only 20 Days Away! appeared first on ELE Times.

Keysight Technologies, Inc. announced that they support the development of the new NVIDIA NVQLink open architecture for the low latency of quantum processors and AI supercomputing.  Keysight Technologies is working with NVIDIA to advance hybrid quantum–AI computing through high-performance control systems and AI-driven infrastructure.

Disaggregated computer architecture is redefining the future of high-performance computing (HPC), enabling organizations to meet rapidly evolving computational demands with greater agility and efficiency. By decoupling compute, memory, storage, and networking into composable resource pools, this approach allows on-demand configuration and precise resource allocation, maximizing performance while optimizing both scalability and cost.

As industries push toward increasingly complex and data-intensive workload spanning artificial intelligence, data analytics, large-scale simulations, and quantum computing—disaggregated systems deliver the flexible, future-proof foundation required to sustain innovation at scale. With seamless upgrades, improved utilization, and dynamic adaptability, these architectures are poised to become the cornerstone of next-generation HPC infrastructure, driving breakthroughs across science and industry.

With decades of experience designing and enabling large-scale systems, Keysight is advancing the integration of quantum and classical computing technologies to address evolving computational challenges. The company’s QCS enables precise, scalable quantum experimentation and plays a vital role in the emerging quantum ecosystem. Working with NVIDIA NVQLink and NVIDIA CUDA-Q, Keysight is exploring how quantum control systems and classical accelerators can be harnessed together, Keysight is helping organizations prepare for a new era of hybrid computing—one that enables quantum-enhanced AI, ultra-precise simulations, and advanced modeling, while remaining adaptable to future advancements across both quantum and classical domains.

This initiative marks a significant milestone for Keysight aimed at uniting high-performance control systems with AI-driven infrastructure to accelerate quantum research and hybrid compute development.

Dr. Eric Holland, General Manager, Keysight Quantum Engineer Solutions, said: “As the industry accelerates toward the next era of high-performance computing, leadership means more than building breakthrough technologies, it requires defining the standards that make these transformative technologies universally accessible. By working with NVIDIA to establish a framework for quantum–HPC hybrid compute, we are helping ensure that tomorrow’s heterogeneous engines, spanning quantum, AI, and classical HPC, operate seamlessly within modern data centers.  Together, we’re shaping the future fabric of compute for scientific discovery and innovation at scale. ”

Tim Costa, General Manager for Quantum at NVIDIA, said: “Driving breakthroughs in quantum computing requires quantum processors to integrate within AI supercomputers to run complex control tasks and deploy hybrid applications. Keysight is playing an integral role in solving this challenge, and NVQLink is the open unified interface for developing what comes next.”

The post Keysight to Showcase Quantum-AI Collaboration at GTC 2025 with NVIDIA NVQLink appeared first on ELE Times.

This is my ongoing build that is working now after i put a lm317/lm337 regulator in it untill i figure out how to build a series pass regulator. Going to add filters on the dc side aswell. Softstart/emi filter/transformer/rectifier, 15000uF + 10000uF + 5630uF with capacitance multiplier and ifcourse the regulator. submitted by /u/Whyjustwhydothat [link] [comments]

I had to stop sorting at this point. My tweezer fingers started to hurt. submitted by /u/Ryzen-Sunn [link] [comments]

Photonic simulation CAD software developer Photon Design Ltd of Oxford, UK has updated its PICWave photonic integrated circuit (PIC) design and simulation tool with an intuitive new graphic user interface (GUI) and an enhanced feature set...

Polyn Technology Ltd. announces the successful manufacturing and testing of its first silicon-implementation of its neuromorphic analog signal processing (NASP) technology. It includes the validation of both the NASP technology and design tools, which automatically convert trained digital neural network models into ultra-low-power analog neuromorphic cores ready for manufacturing in standard CMOS processes. The first product chip features an analog neuromorphic core of a voice activity detection (VAD) neural network model.

(Source: Polyn Technology Ltd.)

This platform uses trained neural networks in the analog domain to perform AI inference with much lower power consumption than conventional digital neural processors, according to the company. Application-specific NASP chips can be designed for a range of edge AI applications, including audio, vibration, wearable, robotics, industrial, and automotive sensing.

This is the first time that Polyn generated an asynchronous, fully analog neural-network core implementation in silicon directly from a digital model. This opens up a “new design paradigm— neural computation in the analog domain, with digital-class accuracy and microwatt-level energy use,” said Aleksandr Timofeev, Polyn’s CEO and founder, in a statement.

Targeting always-on edge devices, the NASP chips with AI cores process sensor signals in their native analog form in microseconds, using microwatt-level power, which eliminates all overhead associated with digital operations, Polyn explained.

Recommended Neuromorphic analog signal processor aids TinyML

The first neuromorphic analog processor contains a VAD core for real-time voice activity detection and offers fully asynchronous operation. Key specs of this NASP VAD chip include ultra-low-power consumption of about 34 µW during continuous operation and ultra-low latency at 50 microseconds per inference.

In addition to the VAD core, Polyn plans to develop other cores for speaker recognition and voice extraction, targeting home appliances, communications headsets, and other voice-controlled devices.

In April 2022, the company announced its first NASP test chip, implemented in 55-nm CMOS technology, demonstrating the technology’s brain-mimicking architecture. This was followed in October 2022 with the introduction of the NeuroVoice tiny AI chip, delivering on-chip voice extraction from any noisy background. In 2023, Polyn introduced VibroSense, a Tiny AI chip solution for vibration monitoring sensor nodes. (Polyn was ranked as an EE Times Silicon 100 company to watch in 2025.)

Customers who are developing products with ultra-low-power voice control can apply online for the NASP VAD chip evaluation kit. Polyn will demonstrate its first NASP chips, available for ordering, at CES 2026 in Las Vegas, Nevada, January 6-9, in Hall G, Booth #61701. A limited selection will be showcased at CES Unveiled Europe in Amsterdam, October 28, Booth HB143.

The post Polyn delivers silicon-implementation of its NASP chip appeared first on EDN.

Printed-circuit boards (PCBs) are an integral part of most electronic devices today, and as PCBs become smaller, electronics engineers must remain aware of the tiny defects that can affect how these components function, especially when they involve high-frequency signals. Surface roughness may seem minor, but it can significantly affect PCB performance, including impedance and signal transmission. What should electronics engineers know about it, and how can they minimize this issue?

Path length

Rough PCB surfaces increase the signal’s path length. This is due to the skin effect, which occurs because high-frequency electrical signals are more likely to flow along a conductor’s outer surface instead of through its core. A longer path length can also increase resistance and cause energy loss.

(Source: Adobe Stock)

Engineers can reduce these issues by choosing the appropriate surface finishes for different PCB parts. Immersion silver is a good choice for balancing performance and affordability, although it must be handled carefully to prevent tarnishing.

Electroless nickel immersion gold offers a flat and smooth surface with a gold layer that promotes excellent solderability and conductivity and a nickel layer that offers oxidation protection. This surface finish minimizes signal distortion, making it a popular option for microwave and radio-frequency applications.

Although immersion tin features a smooth surface, it has lower corrosion resistance than other options, making it less frequently selected for high-frequency PCBs. Because hard gold has good conductivity and resists wear, engineers often use it in high-frequency applications, such as on contact points and connectors. This approach minimizes signal loss and increases overall durability.

If you plan to outsource finishing or other manufacturing steps to a specialty provider, consider choosing one with extensive experience and the equipment and expertise needed for your PCB design.

For example, in 2024, PCB company OKI Circuit Technology created an ultra-high, multilayer PCB line. This expansion boosted its capacity potential by approximately 1.4× while also helping the company cater to customers with smaller orders. The company has also invested in numerous enhancements that increase its precision and equip it to meet the needs of next-generation communications, robotics, and semiconductors.

Signal integrity

Rough surfaces compromise signal integrity and can cause parasitic capacitance. This issue can also increase crosstalk if it results in uneven electromagnetic field distribution. Smoother surfaces enable faster signal speeds while preventing distortion and delays.

Because surface roughness is one of many factors that can interfere with signal integrity, electronics engineers should scrutinize all design aspects to find other potential culprits. Some companies offer specialized tools to make the task easier.

One provider sells software that uses artificial intelligence to assess proposed designs. Users can also check trace path routing by studying cross-sectional diagrams that show various layers, identifying potential issues more quickly.

Component placement and PCB layout configurations can affect signal integrity, so designers should consider those aspects before assuming rough surfaces have degraded performance. Digital twins and similar tools allow engineers and product designers to experiment with various layouts before committing to a final PCB layout. Keeping a log of all design changes also allows engineers to revert to previous iterations if newer versions worsen signal integrity.

If companies notice ongoing signal integrity problems or other challenges, examining the individual industrial processes may highlight the causes. This usually starts with data collection because the information provides a baseline. Once companies begin tracking trends, they can discover the most effective ways to tighten quality control and meet other goals that improve PCB performance.

Tailored assistance

If electronics engineers conclude that rough surfaces are among the primary contributors to signal issues in their high-frequency PCBs, they can then address the problem by partnering with third-party providers that understand the complexities of finishing small parts. These companies can detail the various finish types available and provide pricing and lead times, depending on the unit order of PCBs.

Companies that need PCB finishing for prototypes or small production runs may request manual processes. Skilled technicians use tools and magnification on parts with complex geometries or other characteristics that make them unsuitable for mechanical methods.

Controlled combustion, electrolytic action, and vibratory containers are some of the other options for finishing small parts through non-manual means. Specialist finishers can examine the PCB designs and recommend the best strategies to achieve consistent smoothness with maximum efficiency.

Because many manufacturers have high-volume finishing needs, some startups have emerged to fill the need while supporting producers’ automation efforts. Augmentus is one example, focusing on physical AI to scale automated surface finishing for high-mix environments. The company has built a fully autonomous system for today’s factory floors. In July 2025, the company secured $11 million in a Series A+ funding round to scale for high-mix, complex robotic surface finishing and welding.

Augmentus views surface finishing as one of the most challenging problems in automation, but the company believes its technology will break new ground. Although it is too early to know how this option and others like it may change PCB production, automated processes could offer better repeatability, making surface roughness less problematic.

Ongoing awareness

Because surface roughness can negatively affect high-frequency PCB signals, engineers should explore numerous ways to address it effectively. Considering this issue early in the design process and selecting appropriate finishes are proactive steps for strengthening component quality control.

About the author

Emily Newton is a technical writer and the editor-in-chief of Revolutionized. She enjoys researching and writing about how technology is changing the industrial sector.

The post Are rough surfaces on PCBs impacting high-frequency signals? appeared first on EDN.

Not my first time doing this but my second and for some reason the first tike was successful and this wasnt😂😂 submitted by /u/vIp_bLACK444 [link] [comments]

Epitaxial deposition and process equipment maker Veeco Instruments Inc of Plainview, NY, USA has received multiple orders for its advanced wet processing and lithography systems from a leading specialist foundry. The systems will be deployed for advanced packaging and silicon photonics applications, supporting critical end markets including AI, automotive, aerospace & defense, and communications. Scheduled deliveries for the most recent orders will start in first-quarter 2026...

Skyworks Solutions Inc of Irvine, CA, USA (which manufactures analog and mixed-signal semiconductors) and Qorvo Inc of Greensboro, NC, USA (which provides core technologies and RF solutions for mobile, infrastructure and defense applications) have agreed to merge in a cash-and-stock transaction that values the combined enterprise at about $22bn, creating a US-based supplier of high-performance RF, analog and mixed-signal semiconductors...

The ovens in this two-part Design Idea (DI) can’t even warm that leftover half-slice of pizza, let alone cook dinner, but they can keep critical components at a constant temperature. In the first part, we’ll look at a purely analog approach, saving something PWM-based for the second.

Perhaps you want to build a really wide-range LF oscillator with a logarithmic sweep, using no more than a resistor, an op-amp, and a diode for the log element. That diode needs to be held at a constant temperature for accuracy and stability: it needs ovening (if there is such a verb).

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

I made such a device some years ago, and was reminded of it when spotting how a bead thermistor fitted rather nicely into the hole in a TO-220’s tab. (Cluttered workbenches can sometimes trigger interesting cross-fertilizations.) Now, can we turn that tab into a useful temperature-stabilized hotplate, suitable for mounting heat-sensitive components on? Ground rules: aim at a rather arbitrary 50°C, make the circuitry as simple as possible, use a 5-V supply, and keep the consumption low.

This is a practical exploration of how to use a transistor, a thermistor, and as little else as possible to get the job done. It lacks the elegance and sophistication of designs that use a transistor as both a sensor and a source of heat, but it is simpler.

Figure 1 shows the schematic of a simple version needing only a 2-wire connection, along with two photos indicating its construction. It was slimmed down from a more complex but less successful initial idea, which we’ll look at later.

Figure 1 A simple oven circuit, heated by both R2 and Q2. The NTC thermistor Th1 provides feedback, the set point being determined by R1. Note how critical components are thermally tied together as they are all built onto the TO-220 package, as shown in the photos. Also note the fine lead wires to reduce heat loss once the assembly is heat-insulated.

Both R2 and Q2 can contribute to heating. On a cold start (literally) Th1’s resistance is high so that the Darlington pair Q1 and Q2 has enough base voltage to saturate it, with (most of) the rail voltage across R2. As the assembly heats up, Th1’s resistance drops, reducing the drive to Q1/2. The rail now appears across both R2 and Q2, with the latter taking over as the main, though now reduced, source of heat. This gives a degree of proportional control, reducing the drive as the set-point is approached. That base drive depends not only on the ratio of R2 to Th1 but also on Q1/2’s effective VBE, which needs to be temperature-stabilized—as indeed it is. Consumption varies from ~90 mA when cold to ~30 mA when stable.

Setting and measuring the temperature

R1 sets the stabilization temperature, the target being 50°C. Experimentally, 12k worked best, giving a stable hotplate temperature of 49.6°C for an ambient of 19.5°C. Cooling the surroundings to -0.5°C left the hotplate at 48.8°C, so that the hotplate temperature falls by 0.04°C for each degree drop outside. Better thermal insulation would have reduced that.

The measuring probe was a 10k thermistor equipped with fine wires and stuck to the hotplate with thermal paste, the module being wrapped in ~12 mm of foam—and we’ll come back to that. Thermal paste and heat shrink could have been used for the main assembly but dabs of epoxy worked well and kept the hotplate surface flat. Metal-loaded, high-temperature epoxy conducts heat several times better than the plain-vanilla variety while still being an electrical insulator, though that may make little difference given reasonable physical contact.

Other resistors and transistors

R2 is fairly critical. A higher value than 47R heats up slower than is necessary, while a lower one does so too fast, leading to the temperature overshooting because of the limited proportional control. Experiments showed that 47R was close to optimal, with minimal overshoot and thus the fastest stabilization time. The hotplate temperature settles to within a degree in around two minutes and is almost spot-on after three minutes.

Neither Q1 nor Q2 is critical, but the E-line package of a ZTX300 (for example) fits better than a TO-92 would. But why not use an integrated Darlington like the TIP122? Alas, such devices incorporate base–emitter resistors, nominally 10k and 150R, which load Th1 unpredictably. Trying one picked at random showed that R1 needed to be ~7k8 for a set-point of 50°C.

Similarly, this also works with Q1/2 replaced by a MOSFET, with R1’s value now depending on the gate threshold; 3k9 was close for a BUK553. BJTs are far more predictable: build this as drawn, and it should be within a degree, with Q1/2’s VBE settling at ~1.18 V; use a random MOSFET, and it could be anywhere.

Access all areas

The next variant, shown in Figure 2, is electrically similar but provides access to useful circuit nodes to help monitor its performance. It was also easier to experiment with.

Figure 2 While electrically the same as Figure 1, this brings out most circuit nodes to help with experimentation and monitoring, including the LEDs on “pin 3”.

Now we can see what we’re doing! The LEDs give a simple status indication, the green one lighting when it’s close to the set-point rather than fully stable. Figure 3 shows the effect, along with traces for Q1/2’s Vcc—allowing us to read the current in the transistors and R2—and the hotplate temperature. The latter is accurate, but the voltage and current scales are less so because they assume a precise 5-V supply and a 50-Ω load rather than the measured 4.94 V and 47Ω plus stray resistance. This module stabilized at ~50.6°C.

Figure 3  Measurements taken from Figure 2’s circuit for about three minutes after a cold start.

So much for the basic circuit. Now, it needs thermal insulation to keep the heat in, a block of foam being the obvious choice. But foams have widely differing thermal conductivities. Expanded polystyrene or polyethylene will work, but the foamed polyisocyanurate or similar used for wall insulation panels is around twice as good—and offcuts are often freely available from builders’ skips/dumpsters! Figure 4 shows the module from Figure 2 mounted on/in a block of it, with at least 10 mm of foam around any part of the circuit module.

Wikipedia has an illuminating plot of the thermal conductivities of many materials, including our foams and epoxies. The article of which it is a part has a lot of useful background, too.

Figure 4 The module from Figure 2 mounted on a block of foam. The intermediate connecting wires are meandered across its surface to minimize heat loss. Note the diode, typical of a component needing stabilization, stuck to the hotplate, ready for its new connections to be treated similarly.

The fine lead wires—0.15 mm diameter, as used with wiring pencils—are meandered over the surface to lengthen the thermal paths. Copper has a thermal conductivity some 19,000 times greater than the foam: 384 W/m·K vs ~0.02 W/m·K. In very crude terms, for a given thermal path length and temperature gradient, a single, short 0.11-mm-diameter copper wire will leak heat at about the same rate as the entire surface area of our foam block (~6000 mm2). Ironically, perfect insulation would be bad, as the innards could never cool to recover from an overshoot. This build took 620 seconds to cool by 63% of the way to ambient.

Hot stuff

Disconnecting Th1 in Figure 2’s circuit let the module heat up to the max while still allowing monitoring—or would have done, had I not chickened out when its resistance dropped to 720 Ω, for just over 100°C. (The epoxy was rated to 110°C.) That was with the full insulation; in free air, it struggled to reach 70°C—the rating for other components.

One subtle problem is the inevitable mismatch between the sensing thermistor and the target device, as analyzed in a Stephen Woodward DI, which also implies that the position of the target on the hotplate will affect its actual temperature. We’ll ignore that for the moment, because we’re more interested in constancy than precision, but will return to it in Part 2.

Finishing at the starting point

The foregoing circuits were actually simplifications of my starting point, which is shown in Figure 5. When the temperature is stable at ~50°C, point A is at half-rail. R3 is chosen so that U1’s output will turn Q1/2 on just enough to maintain that. However, while the extra gain improves the temperature regulation, it also causes some overshoot. R3 or R2 must be trimmed to set the temperature: fiddly, and not really designable. R3 was calculated at 4k12 but needed ~5k6 in reality. That’s why I gave up on this approach.

Figure 5 The original circuit that suffered from overshoot. The LEDs give a too-high/too-low temperature indication.

The long-tailed pair of Darlingtons (Q3, Q4) sense the difference between the thermistor voltage—half the rail when stable, as noted—and a half-rail reference, so that the red LED will be on when the temperature is low, the green one lighting while it’s high, with both on at the stable point. Full-red to full-green takes ~300 mV differential, or ~±3°C. This works but gives no better indication than the LEDs in Figure 2. (The low-power Darlingtons used seem to omit those extra, internal resistors. Q1/2 could now be replaced by that TIP122, as it’s driven by a low-impedance source. R4 is purely to protect against current surges.)

Figure 6 plots its performance when starting from cold, showing the overshoot and recovery. Compare this with Figure 3.

Figure 6 The start-up performance of Figure 5’s circuit.

If I were building something similar in any quantity, I wouldn’t do it like this: SMDs and a flexible circuit would be much cleaner. For example, a 2512 power resistor for R2 (or R5 in Figure 5), pressed flat, with some insulation, against the power transistor’s tab would probably be ideal.

In Part 2, we’ll see how even a simple PWM-based circuit can give better proportional control and hence generally better performance. The bad news: we may eventually abandon the TO-220 tab in favor of another way of assembling our hotplate.

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