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I am really autistic about the precision of temperature in my projects, and I found a cheap toaster oven for 14$ the perfect size for my work space, decided to replace the bimetallic thermostat with custom electronics and control circuitry, it was an amazingly fun project! hope you all enjoy this dumb project! and remember, if you mess with 120V BE CAREFUL! programming listed on github, video of process posted to youtube https://github.com/EleriLove/pwm-pid-control-of-relay- https://youtu.be/Qr83flDN-Bk submitted by /u/JarrekValDuke [link] [comments]

I’m always interested in how researchers, scientists, engineers, and manufacturing specialists leverage apparently unrelated advances to their own advantage for devising innovative techniques and advances. This phenomenon is not new, of course; it’s been one of the driving forces behind technological advances for hundreds of years in situations ranging from fairly modest to some that are impressively esoteric.

This seems to be the case especially for sophisticated sensors. The latest one I have seen addresses the challenge of detecting terahertz energy. It’s not news to this audience that the terahertz region of the electromagnetic spectrum, generally defined as 100 GHz to 10 THz, has many barriers when developing complete viable systems. It’s informally called the “THz gap” where the frequencies are too high and the wavelengths too short for most electronic components; yet too low and too long, respectively, for using optical ones.

A deep-physics concept

Addressing this issue, a joint team at the University of Cambridge and Swansea University (both in the U.K.) took advantage of some “new” physics. Their sensor merges a metasurface with a recently discovered quantum physics effect that is a spinoff of the well-known photoelectric effect.

In the photoelectric effect, there is the emission of electrons (current) from a material (usually a metal) when it’s struck by electromagnetic radiation such as light with high-enough energy. In contrast, their terahertz detector makes use of the in-plane photoelectric effect (IPPE), where incoming terahertz photons transfer energy to electrons that are confined within a two-dimensional electron gas. Those energized electrons cross a carefully designed potential step, generating an electrical current that can be measured.

If you are not familiar with the photoelectric effect, it has a major place in the history of modern quantum physics. The effect had been observed since the latter half of the 1880s with solid experimental data, but non-quantum physics could not explain the data and in fact was at odds with the data. Albert Einstein proposed a radically new explanation in his seminal 1905 paper “On a Heuristic Viewpoint Concerning the Emission and Transformation of Light”—one of four truly significant papers he had published in that “miraculous year”, which resulted in the Nobel Prize award in 1921.

That photoelectric effect, however, is different in the in-plane version, a phenomenon that was observed only recently, in 2022 (“An in-plane photoelectric effect in two-dimensional electron systems for terahertz detection”). In the IPPE quantum process, incoming terahertz photons transfer energy to electrons confined within a two-dimensional electron gas. Those energized electrons cross a carefully designed potential step, generating an electrical current that can be measured.

Unlike conventional photoelectric detectors, this mechanism does not require photons to exceed a minimum energy threshold. Because the process occurs entirely within the plane of the material, it also avoids several efficiency limitations that affect traditional detector designs.

While earlier detectors using this concept showed promising performance, they suffered from one major drawback. They captured only a small portion of the incoming radiation because they relied on single-antenna structures.

A new approach

To overcome that limitation, the team devised a metasurface with a patterned structure that concentrates electromagnetic energy into regions much smaller than the wavelength of the incoming radiation. In the new design, a repeating “brickwork” pattern gathers terahertz waves and channels them into narrow gaps where detection takes place (Figure 1).

Figure 1 Schematic diagram of the MetaPETS detector with the brickwork metamaterial (a). Zoom-in view showing a unit cell indicated by a black dashed rectangle. The unit cell sizes in the 𝑥- and 𝑦-directions, 𝑑𝑥 and 𝑑𝑦, are indicated (b). Zoom-in view highlighting the capacitive gap, illustrating the width 𝑤 of the capacitor in the lateral (𝑥)-direction, and the gap size 𝑔 of the capacitor. The 2DEG is depleted along the edge of the mesa, as indicated, causing the area of the conducting 2DEG to be offset inwards from the lithographically defined mesa edge (c). Zoom-in view highlighting the summation of photocurrents generated by the PETS detection elements at the lower right corner. The electron flow occurs within the 2DEG, and its direction is shown by the red arrows. Without loss of generality, we show an example with gate 1 positively biased and gate 2 negatively biased (d). Photocurrent detection circuit (e). Source: SPIE Digital Library

Each of these tiny gaps functions as an individual detector. By distributing many of them across the metasurface and connecting them electronically, the researchers were able to combine their outputs into a stronger overall signal.

But that was only the first step. Individual photoelectric tunable-step (PETS) detector elements were then integrated into the capacitive gaps, as they experience the strongest electromagnetic fields. This ensures optimal coupling of the metasurface to the detection elements and significantly boosts the detection sensitivity compared to simply connecting the elements in parallel.

Fabrication relied on a semiconductor structure containing a high-mobility electron gas. The manufacturing process closely resembles techniques already used to produce field-effect transistors, making future integration with electronic circuits more feasible. Since the metasurface itself concentrates the incoming radiation, the detector does not require external focusing components such as silicon lenses and their precise alignment, which simplifies assembly, reduces cost, and could make large-scale manufacturing easier.

In tests, the detector was cooled to 10 K and exposed to radiation near 1.9 THz. It generated a clear electrical signal that matched the on and off pattern of the incoming radiation. Measurements showed a responsivity of 2.7 amperes per watt. The proof-of-concept device also achieved an external quantum efficiency of 2.1% at 1.9 THz.

Figure 2 Time-averaged photocurrent in response to the 1.881 THz quantum cascade laser (QCL) radiation, measured in the source-drain circuit while mechanically blocking and unblocking the THz waveguide (WG). The two gates are biased at +0.76 V and −0.095 V, respectively (a). Real-time photocurrent measurement using an oscilloscope, showing the response to a pulse emitted by the QCL during its on time with 100 sweeps averaged. The two gates are biased at +0.76 V and −0.095 V, respectively (b). 2D maps are shown as a function of the two gate voltages: photocurrent (c) and four-wire conductance of the device (d). Source: SPIE Digital Library

According to the researchers, this represents roughly a 20-fold improvement over previously demonstrated PETS detectors. Much of that gain comes from the metasurface’s ability to capture more incoming radiation and direct it into the active detection regions.

The work is described in both theory and practical facets in their readable paper “Quantum metasurface-based photoelectric tunable-step terahertz detector” published in Advanced Photonics. Of course, they note that there’s more work to be done. The researchers believe the technology could eventually operate at temperatures higher than those required by many competing detector designs. Similar PETS devices have already demonstrated performance at temperatures reachable using compact cryocoolers rather than liquid helium.

Have you used a highly sophisticated sensor that blends advanced technologies, or had to innovate and implement such a sensor yourself? What were the unexpected challenges you encountered?

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 Metasurface plus photoelectric quantum effect yields sensitive THz detector appeared first on EDN.

With the explosive growth of artificial intelligence and the exponential increase in computing power demand, data centers require greater bandwidth but without increasing power consumption further. The short-distance interconnect solution inside data-center servers and racks is currently undergoing a historic shift of ‘optical-in, copper-out’...

Synthetic diamond materials firm Element Six of Oxford, UK (E6, part of the De Beers Group) and Tokyo-based Orbray Co Ltd (which makes precision jewel parts, DC coreless motors, fiber-optic components, and medical devices) have announced the next phase of their partnership, building on a joint aim to deliver wafer-scale single-crystal diamond (WSC)...

Multiple domains provide designers and test engineers with complementary views of acquired signals.

Oscilloscopes originated as instruments that measure electrical signals in the time domain. The advent of the digital oscilloscope, where input waveforms were digitized and stored in the instrument, opened the door to other analysis domains. In addition to the time domain analysis of the signal, these include the Fast Fourier Transform (FFT) for frequency domain analysis and histograms for statistical analysis. These three measurement domains provide designers and test engineers with three complementary views of the acquired signals. This article reviews the three domains and provides an example of how they interact to facilitate problem diagnosis.

The time domain

The time domain plots the signal’s voltage versus time. This was the original, and still is the primary, function of the oscilloscope (Figure 1).

Figure 1 This time domain view of a sine wave, an oscilloscope’s original and still primary function, also includes some standard time domain measurement tools.

The time domain view shows the waveform as a function of time. The vertical axis is typically measured in volts, but it can be rescaled to measure in any relevant units when used with appropriate sensors. The vertical channel is scaled in volts per division. The horizontal axis is time in seconds, with the scale set to seconds per division. Time-domain measurements can be as simple as counting vertical and horizontal boxes between significant events on the displayed trace and multiplying by the appropriate scale factor, as we did in the “Before Computer (BC)” era.

Most modern oscilloscope users prefer using cursors or automatic measurement parameters. The figure shows the use of both. The cursors (vertical dashed lines) are set to measure the width of the waveform at the half amplitude of a positive half-cycle. Measurement parameters P1 through P4 measure the amplitude, frequency, period, and RMS amplitude of the waveform, respectively.

The frequency domain

A digital oscilloscope acquires and stores the input waveforms. Having the digital representation of the waveform internally allows the oscilloscope’s analysis tools to process the data. One of the most commonly used tools is the Fast Fourier Transform (FFT). The FFT converts the acquired time-domain data into the corresponding frequency-domain data, also known as a spectrum. This enables the oscilloscope to perform most of the common measurements typically found in a spectrum analyzer (Figure 2).

Figure 2 The FFT spectrum of the sine wave plots signal power versus frequency, showing the frequency content of the acquired signal.

The spectrum of a sine wave shows a spectral line at the fundamental frequency, 10 kHz in this example. Due to the large dynamic range of the frequency domain view, it defaults to a logarithmic vertical axis. This scale is calibrated to read in decibels relative to one milliwatt (dBm). If the acquired signal were a perfect sine wave, then the fundamental is all there would be. In the real world, signal sources are not perfect and also show noise and harmonics. This signal has spectral lines at the third (30 kHz), fifth (50 kHz), and seventh (70 kHz) harmonic frequencies.

The frequency domain has the same measurement tools as the time domain, namely, cursors and measurement parameters. The measurement parameters include many that are specific to frequency domain measurements. In this example, the spectral peak amplitudes and frequencies are read out for the fundamental and third harmonics. The fundamental frequency is 10 kHz (P2), and its amplitude is -7.5 dBm (P1). The third harmonic at 30 kHz (P4) has an amplitude of -84.9 dBm (P3), about 77 dB below the fundamental. The dashed lines on the display show the measurement markers for the parameters, indicating what each measures.

The statistical domain

The statistical domain consists mainly of histograms and persistence trace statistical displays. The histogram is the principal tool of statistical analysis. The persistence trace displays show the statistical mean, standard deviation, and range for each point along a trace. The histogram counts the number of amplitude values within a narrow range (called a bin) as a function of amplitude. The vertical axis is the number of samples in a bin, and the horizontal axis is amplitude (Figure 3).

Figure 3 The amplitude histogram of a sine wave is the principal tool of its statistical analysis.

The histogram of a sine wave has a saddle shape, higher on each end and lower in the middle. The reason for this shape lies in the signal’s rate of change. The oscilloscope samples the incoming signal at a fixed rate. If the input signal has a non-uniform rate of change, there will be a greater number of samples where the signal’s rate of change is slower and fewer samples when the rate of change is faster.

In the case of a sine wave, the rate of change is the slowest at both the positive and negative peaks, and greatest at the zero crossings. Hence, the histogram has the greatest concentration of values at the negative (left side) and positive (right side) values. The zero crossings, located in the center of the histogram, have the lowest concentration of values.

The measurement parameters in the statistical domain include the mean, mode, median, standard deviation, and range, among others. The mean, standard deviation, and range of the histogram in the figure are displayed. The mean is the average value of the waveform, the range is the peak-to-peak value, and the standard deviation is the RMS value for a zero mean (some oscilloscope suppliers list it as AC RMS).

Measuring in three domains

Measuring distortion in an amplifier is a common application for an oscilloscope. Consider a measurement of crossover distortion in a push-pull amplifier that uses two transistors to drive a load (Figure 4).

Figure 4 This simplified overview of a push-pull amplifier is accompanied by a diagram of crossover distortion.

The push-pull amplifier uses two complementary transistors to drive a load. The upper NPN transistor supplies current to the load on the positive half of the input signal. The PNP transistor drives the load for the negative half of the input signal. The transistors conduct alternately, based on the polarity of the input signal.

This type of circuit is commonly found in various applications, including amplifiers, half-bridge and full-bridge power supply circuits, and even totem pole output circuits in ICs. The advantage of push-pull operation is that it has no quiescent power dissipation; when the input is zero volts, no power is delivered to the load. A measurement of a properly operating push-pull amplifier (simulated) shows nearly ideal performance (Figure 5).

Figure 5 The analysis of a properly functioning push-pull amplifier shows nearly ideal performance.

Applying a 10 kHz sine wave to a simulated amplifier and analyzing the output yields the expected results across all three measurement domains. The top trace is the time domain view, expanded using horizontal zoom in the trace below it. The trace is a smooth sine wave with no non-monotonic areas. The positive and negative half cycles have a symmetrical shape. There is no obvious clipping or limiting.

The frequency spectrum displays a fundamental spectral line at 10 kHz, accompanied by small odd harmonic distortion products at 30, 50, and 70 kHz, all of which are below -90 dBm. The statistical domain view is a histogram that exhibits a classical, symmetrical saddle-shaped distribution.

One thing that cannot be allowed in this type of amplifier is for both transistors to conduct simultaneously, which would short-circuit the positive and negative power buses. Most amplifiers use a considerable amount of circuitry to prevent this from happening. This compensation can cause one transistor to turn off before the other turns on, resulting in a nonmonotonic flat spot in the output waveform. This situation is described as crossover distortion, which was shown in Figure 4.

What does crossover distortion look like in the three measurement domains? See Figure 6 for an example.

Figure 6 Crossover distortion is evident in all three measurement domain views.

The figure shows a waveform simulating crossover distortion. The distortion is evident in the zoom trace at the zero-crossing points. In this example, the distortion is exaggerated to make it obvious in the time-domain waveform. The frequency spectrum still shows the 10 kHz fundamental, but the levels of the odd harmonics have increased in both number and amplitude.

The FFT spectrum is excellent at showing the presence of distortion as an increase in harmonic amplitudes. The harmonic amplitudes are in the range of -65 to -70 dBm, about 20 to 25 dB higher than the previous levels of normal operation. What the spectrum doesn’t indicate is the source of the distortion.

Look at the histogram in the bottom trace in the figure. Note the large spike at the zero crossing. This indicates a large number of sample values at the 0-volt level, an indicator of crossover distortion. Other forms of distortion would appear in different areas. If the histogram is not symmetric when folded about the zero amplitude axis, then a non-linearity, such as limiting, is suspected. If either peak shows a significantly larger number of values, then clipping is indicated.

Conclusion

The three domain views provide a three-dimensional interpretation of the analyzed waveform. If the distortion is large enough, it is visible as asymmetries and discontinuities, as exemplified by the step in the time-domain zoom trace. Typically, significant distortion does not necessarily produce visible anomalies in the time-domain view.

The increased harmonic levels in the FFT provide a good indicator of any distortion, but it doesn’t indicate the specific type of distortion. The statistical view in the histogram provides information about the type of distortion.

Arthur Pini is a technical support specialist and electrical engineer with over 50 years of experience in electronics test and measurement.

Related Content

• Basic oscilloscope operation
• Understanding FFT vertical scaling
• Analyze noise with time, frequency, and statistics
• FFTs and oscilloscopes: A practical guide
• How to perform histogram analysis on your oscilloscope

The post Using an oscilloscope’s time, frequency, and statistical measurement domains appeared first on EDN.

PicoJool Inc of Palo Alto, CA, USA (which is developing optical chips and modules for high-bandwidth, low-cost connectivity in hyperscale AI data centers) is introducing its 200G vertical-cavity surface-emitting laser (VCSEL) products, with a bandwidth exceeding 37GHz. The firm will begin sampling chip-level products in third-quarter 2026, including quad 100G, quad 200G and 32x50G NRZ micro-VCSELs for slow and wide applications. PicoJool says that it is already working with system startups and hyperscalers to define the next generation of pluggable, near-packaged optics (NPO) and co-packaged optics (CPO) solutions for AI data centers...

• Industrial-grade vibration sensor delivers the latest wide-bandwidth and dynamic range sensing.
• Intelligent sensor processing unit (ISPU 2.0) inside the sensor boosts signal processing and edge AI performance while reducing energy consumption.
• Provides the first compelling alternative to piezosensors for condition monitoring, combining performance, lightweight design, ease of integration, ultra-accuracy, and energy efficiency.

A global semiconductor leader serving customers across the spectrum of electronic applications introduces an intelligent vibration sensor designed for industrial condition monitoring that requires high accuracy, reliability, and energy efficiency.

Built using ST MEMS (Micro Electromechanical Systems) technology, the IIS3DWB10IS vibration sensor with intelligent sensor processing unit (ISPU 2.0) brings advance digital signal processing and AI inference closer to the sensing element. The result is a compact, rugged device that measures vibrations and shocks up to 200g at frequencies of 10 kHz and above. Combining digital precision and ease of use with a wide temperature range up to 125°C to withstand harsh conditions, the vibration sensor is engineered to help customers improve equipment uptime, reduce unplanned downtime, and support predictive maintenance strategies across industrial environments.

Vibration analysis is the dominant segment in condition monitoring, as many industries use rotating and oscillating machinery for cutting, shaping, moving, cooling, and other processes. The ability to prevent equipment stoppages through early detection of issues, such as predicting bearing failures in advance, helps companies across all sectors, including automotive and other manufacturing activities, to optimize production flow.

“Our industrial MEMS vibration sensor delivers the dynamic range and bandwidth needed for high-end applications and extends the advantages of ST in-sensor digital processing. Integrating the ISPU 2.0, with its new hardware accelerators for fast signal processing and AI inference, sharpens equipment-wear recognition while reducing latency and power consumption,“ said Simone Ferri, APMS executive VP MEMS sub-group. “Industries can expect a new generation of condition monitoring sensors, the first compelling alternative to piezosensors, that is lightweight, easy to fit and design, ultra-accurate, and energy efficient enough for battery-powered operations.”

“The IIS3DWB10IS delivers unique properties for our target markets and environments. Its high dynamic range, wide bandwidth, and high-temperature capability, combined with ease of adoption and a cost-effective, simplified circuit design, allowed us to replace the incumbent piezosensor technology. Moreover, the integrated ISPU 2.0 processor positions complex signal processing and rapid AI inference close to the sensing element, enabling smarter system responses,” said  Andrea Torcelli, Chief Technology Officer at Bonfiglioli S.P.A.

By enabling predictive and prioritizing maintenance, remote condition monitoring allows companies to improve equipment uptime and operating efficiency while eliminating unexpected failures and enhancing safety. Fortune Business Insights states the global market for this technology will exceed $5 billion by 2032, growing at over 9% CAGR1.

 

Further technical information:

The IIS3DWB10IS vibration sensor is the first digital sensor with wide bandwidth and embedded processing to deliver performance meeting the needs of high-end industrial condition monitoring applications, offering a compelling alternative to piezoelectric sensors.

Accurate measurement of vibrations above 10 kHz, with a large dynamic range up to 200g, combines with a noise floor as low as 35 µg/sqrt(Hz). This is comparable to the noise performance of piezoelectric sensors. Moreover, the IIS3DWB10IS delivers equivalent accuracy and sensitivity, adding digital-sensing advantages including smaller size, lower power consumption, simplified electrical and mechanical design, and greater flexibility in the computational partitioning.

ISPU 2.0 (Intelligent Sensor Processing Unit) introduces new hardware accelerators to perform real-time signal processing and AI at the edge. These hardware accelerators make frequently used functions faster and more power-efficient. The core is C-programmable and contains on-chip program and data RAM.

The supporting ecosystem provides software libraries that facilitate executing typical vibration monitoring algorithms in the ISPU, including FFT, filtering, envelope, velocity severity, and anomaly detection. With 40 MIPS and 40 MFLOPS digital signal processing, the ISPU 2.0 delivers up to four times the processing performance of the previous generation. In addition, the ISPU 2.0 sensor interface supports six times faster data transfer with the MEMS circuitry.

The IIS3DWB10IS also contains a 2048×80-bit FIFO register and an accurate temperature sensor. The sensor’s rugged MEMS-based design supports operation up to 125°C. The IIS3DWB10IS is supported in ST’s 10-year industrial longevity program. The IIS3DWB10IS is a 4.5 mm x 4.5 mm x 1.5 mm 16-lead LGA package with wettable flanks that facilitate automatic optical inspection in high-quality surface-mount assembly processes. The product is scheduled to be available by July 2026 from $25 for orders of 1000 pieces. 

The post STMicroelectronics High-Performance Vibration Sensor offers an alternative to Piezosensors appeared first on ELE Times.

Fabless firm Cambridge GaN Devices Ltd (CGD) — which was spun out of the University of Cambridge in 2016 to design, develop and commercialize power transistors and ICs that use GaN-on-silicon substrates — has joined forces with NXP Semiconductors N.V. of Eindhoven, The Netherlands to accelerate time to market in data-center and automotive markets...

I’ve been working on a custom Arduino wireless remote using an NRF24L01 module and a rotary encoder. The photos show how the project evolved from a breadboard prototype to a fully soldered perfboard version. The next step is adding an I2C LCD and refining the software. I’d love to hear any suggestions or ideas before I move on to the next revision. What would you add or change? submitted by /u/mAbdelazim01 [link] [comments]

Wolfspeed Inc of Durham, NC, USA — which makes silicon carbide (SiC) materials and power semiconductor devices — has appointed Daniel (Dan) Whalen as vice president, investor relations, leading investor relations strategy and engagement with the financial community as the firm continues to advance its long-term strategic and operational priorities...

Epiwafer and substrate maker IQE plc of Cardiff, Wales, UK and specialty analog foundry Tower Semiconductor Ltd of Migdal Haemek, Israel have announced a multi-year agreement for the supply of indium phosphide (InP) epiwafers for optical connectivity solutions serving AI-driven data-center infrastructure...

The search for novel ways to treat cognitive, sensory, and motor disorders, and their associated impairments—from restoring movement in people with paralysis, and enabling intuitive control of prosthetic limbs, to re-establishing speech and vision—is in full swing. In parallel, neuroscience is pushing for more powerful tools to probe neural dynamics and unravel the mechanisms underlying cognition.

These advances are increasingly driven by brain-computer interfaces (BCIs), which directly connect the brain to electronic systems and hold enormous promise for both transformative therapies and deeper scientific insight.

Cortical BCIs—systems that record electrical activity from the cortex—come in a variety of flavors. Intracortical BCIs (iBCIs), for instance, employ microelectrode arrays (MEAs) implanted within the cerebral cortex, while electrocorticography (ECoG)-based systems place electrodes on the cortical surface, between the skull and the brain tissue. Despite their differences, both aim to capture fine-grained electrical activity from large populations of neurons. But as the number of underlying recording channels increases, so does the volume of neural data that must be transmitted and processed.

The challenge: this data surge drives up power consumption and, consequently, heat generation—while even small temperature increases can irreversibly damage neurons. As a result, lossless data reduction and compression become essential, cutting the number of transmitted bits without compromising the fidelity of the underlying neural information.

Why ‘simply’ increasing MEA recording channels and bandwidth isn’t enough

Scaling BCIs is a deeply complex, system‑level challenge. Let’s take iBCIs as an example. First, at the recording front-end, MEA neural probes must continue to grow in channel count to eventually reach several thousand parallel electrodes—well beyond the 1,536 channels offered by today’s Neuropixels 2.0 Quad Base (QB).

At the opposite end of the system, iBCIs must sustain high-bandwidth, low-latency communication with (external) decoding and processing hubs. Here, impulse-radio ultra-wideband (IR-UWB) has emerged as a promising technology. Beyond eliminating the usability and comfort constraints of wired links, IR-UWB combines electromagnetic regulatory compliance with data rates above 124 Mbps over distances of tens to hundreds of centimeters, low power (roughly 30m W, which is around 10x lower than Wi-Fi), strong interference resilience, and inherent physical-layer security.

Still, even the most advanced UWB links cannot meet the bandwidth requirements imposed by future, high-density MEAs. Streaming raw data from an existing 1,500-channel probe such as the Neuropixels 2.0 QB demands throughputs well over 500 Mbps, far beyond UWB’s practical operating range. Pushing toward 10,000+ parallel channels only widens this gap.

These bottlenecks shift the pressure onto the on-chip compression technology that bridges the MEAs (or any other recording devices) and the wireless interface. Concretely, it will need to incorporate advanced, lossless data reduction to dramatically shrink data volume while preserving the full dynamic range and information content of the recorded signals. Unfortunately, conventional strategies rely on large memory buffers, heavy digital logic, or lossy approximations, rendering them unsuitable for use in heavily constrained iBCIs.

An NCT chip for lossless data reduction

To meet the data-rate, power, and thermal constraints of next-generation iBCIs, imec has developed a new neuromorphic compressive telemetry (NCT) chip for lossless, real-time data reduction. The architecture is built around two key innovations.

• Send-on-delta signal acquisition, replacing traditional Nyquist-rate sampling with an event-‑driven scheme that produces data only when the neural signal changes.
• A ternary packet-based AER serializer (eSER), which groups these events into compact packets for efficient serialization and deterministic transmission.

Together, these building blocks allow the NCT to eliminate redundant data, thus lowering iBCIs’ power and bandwidth requirements, while preserving all the information needed for high-fidelity spike reconstruction.

Send-on-delta encoding for lossless, event-driven signal acquisition

Most cortical neurons fire surprisingly infrequently, typically less than 10 hertz, meaning just a few dozen spikes per second (and often even less). This inherent sparsity presents a major opportunity for data compression and reduction.

Traditional Nyquist-rate sampling captures signals at a fixed frequency—commonly 20-30 kHz for neural sensing—regardless of whether any neural event or spike is actually occurring. This produces a continuous stream of samples, the vast majority of which are redundant (when neurons are silent).

Imec’s send-on-delta sampling/encoding approach takes a fundamentally different path. Instead of sampling at fixed intervals, send-on-delta proposes an event-based, signal-dependent temporal sampling scheme: data is generated only when a signal changes by more than a predefined threshold (Δ). Thus, the output is not a dense waveform, but a sparse stream of information-rich events.

This brings several advantages: drastically fewer data points (often by an order of magnitude), significantly lower power consumption, and much lower bandwidth needs, while all spikes are captured with high fidelity.

A key improvement in imec’s latest (second-generation) send-on-delta mechanism is that the encoding now operates fully in the digital domain. Instead of starting from raw analog voltages passing through a power-hungry send-on-delta analog-to-digital converter (ADC), the system works with a digital-state representation that reflects meaningful changes in the neural signal. In simple terms, send-on-delta digitally detects when the signal changes, and then decides what to do with the underlying data.

A ternary packet-based AER protocol for advanced packetization and serialization

While imec’s send-on-delta approach effectively exploits the sparsity of neural activity, it naturally produces spike-driven data streams (only when neural signals change, not at fixed intervals). This is desirable to achieve power savings, but it requires a communication method that can handle irregular, spike-driven data.

Address-event representation (AER) protocols are a common solution for spike-driven event communication. However, existing AER schemes show several limitations when applied to high-density neural recordings. For example, when multiple readout channels generate events at the same time, classical AER relies on event arbitration or acknowledgement-based handshaking, which does not scale well to large channel counts and introduces unpredictable latency.

In addition, neural spikes exhibit strong spatial correlation—a single spike may appear across several adjacent electrodes—yet traditional AER methods packetize and serialize each event independently, repeatedly transmitting redundant address information and incurring unnecessary protocol overhead.

To overcome these limitations, imec developed an event-based serializer (eSER) that combines send-on-delta with a ternary packet-based AER protocol, which is purpose-built for neural telemetry. Imec’s design introduces several key advantages:

• Event-driven serial transmission only when neural activity occurs.
• Spatial grouping of correlated events, sending one compact packet instead of many little messages, which eliminates redundant metadata and reduces protocol overhead by up to a factor of two.
• No need for arbitration or collision-handling logic; rather than arbitrating between simultaneous events, the eSER first collects all Δ outputs and then emits one packet in a controlled sequence. This completely avoids the event collisions, while removing the need for complex arbitration circuitry with indeterministic latency, a major bottleneck in conventional AER.
• With rich, multi-bit (ternary) encoding for lossless reconstruction, imec’s AER packets contain Δ values, direction of change, and the channel ID to enable lossless spike waveform reconstruction (even for low amplitude spikes down to ~31 µV).

As such, imec’s AER solves the scalability, complexity, overhead, indeterministic latency, and power concerns of traditional implementations by aligning communication with the true nature of neural signals—sparse, bursty, and spatially correlated. By intelligently grouping events, encoding richer Δ information, and activating the serializer only when needed (when Δ does not equal zero), the system filters out redundant data at the source and achieves dramatically higher compression and ultra-low power operation.

Validating high‑fidelity, low‑power telemetry using neural recordings

To evaluate its performance, imec tested its NCT chip—fabricated on 65-nm CMOS—using in-vivo neural recordings from high-density datasets.

In these experiments, the system successfully digitized, compressed, packetized, serialized, and reconstructed neural activity from 384 recording channels in real time. Powered by imec’s send-on-delta approach and ternary packet-based AER scheme, the chip consistently achieved more than a ten-fold reduction in data volume, even after accounting for the packetization overhead.

Crucially, this level of compression was achieved without compromising spike fidelity. The system preserved spikes with amplitudes down to 31 µV, reconstructing them with <23% normalized RMS error, equivalent to a signal-to-noise and distortion ratio (SNDR) of 12.7 dB, which is well in line with the commonly accepted threshold for reliable spike sorting. In other words, the compressed, serialized data stream retained all waveform features essential for downstream neural decoding (and analysis).

The complete NCT telemetry chain operates at exceptionally low power (consuming just 0.1 µW per channel) and demonstrates record-breaking silicon efficiency, requiring only 27 bits of memory per channel, a 55-fold reduction compared to epoch-based compression schemes that rely on kilobits of buffer memory. This dramatically smaller memory footprint minimizes silicon area, lowers both leakage and dynamic power, and helps keep implant temperatures safely within clinical limits.

Importance of deterministic latency in distributed neural implants

Neural spikes are extremely brief—often well under 200 µs in duration—with their precise timing carrying essential information about how the brain encodes movement, perception, and intent. In distributed (intra) cortical systems, where multiple recording channels record from different cortical regions at once, even small variations in transmission delay can distort the temporal relationships between spikes. To preserve these relationships, the telemetry system must maintain deterministic latency, with timing uncertainty kept to just a few microseconds.

Imec’s NCT architecture achieves this requirement by design. By eliminating arbitration delays, and avoiding global clock distribution, the system ensures that data from each sensor unit is aligned in real time. Measurements show a latency variation well below 10 µs, comfortably meeting the microsecond-level precision needed for distributed spike-timing analysis. As recording channels scale and become increasingly spatially distributed, this deterministic timing ensures that neural activity can be reconstructed accurately across thousands of channels, without temporal drift or distortion.

Next step: Scaling toward 10,000 channels

Imec’s most recent results show that its neuromorphic compressive telemetry architecture can already scale to 1,500 channels—on par with today’s highest-density MEA platforms—while delivering a 10x data reduction and maintaining high-fidelity spike reconstruction. This confirms that the core principles—the event-driven ‘send-on-delta’ signal acquisition, and ternary AER packetization/linearization—extend far beyond the initial 384-channel tests.

As a next step, the team is now complementing the NCT chip with an AI-enhanced auto encoder to identify the ~1% of neural events that carry the most behavioral or clinical relevance. By selectively encoding and transmitting only this most informative subset, imec’s NCT architecture is projected to reach a 100x data reduction, unlocking practical scaling toward 10,000 recording channels.

Yao-Hong Liu, scientific director at imec, is a recipient of European Research Council (ERC) Consolidator grant. He is also a guest professor at Delft University of Technology. His current research focuses on wireless communication and neuromorphic compression for implantable brain computer interfaces (BCIs) and robotic sensing applications.

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The post Neural telemetry: New chip delivers 10x compression while preserving signal integrity appeared first on EDN.

Got lightning? A bidirectional RJ45/SFP intermediary can, by “taking one for the team”, keep it from propagating through the remainder of your network.

Back in November 2024, I detailed my initial attempts (with underwhelming results) to figure out some way to avoid using the two lightning-prone spans of Ethernet cable running around outside my house (which I’d inherited when I bought the place, mind you; the bad idea wasn’t mine in the first place!) and without replacing them with expensive- and complicated-to-install alternative cable runs inside the house. And speaking of lightning, we’re nearing the start of Monsoon Season 2026 as I write these words in mid-May…

…but I won’t be gritting my teeth quite so intensely this year, thanks to reader Steve Strobel:

You don’t need fiber from your ISP to protect 99% of your equipment from surges on their connection. After their modem/router, you can convert to fiber, back to Ethernet, then go to the rest of your network. A pair of gigabit Ethernet/fiber media converters (for example, TP-Link MC220L, about $21 each) and a foot of fiber should do the job. Or if your switch has a SFP port, drop an SFP fiber transceiver in that and you need only one converter.

Here’s my response:

You are brilliant! What I’ve just realized thanks to your comments is that if I put a pair of these at each of the endpoints of each of the two external Ethernet spans (eight media converters total), along with four short spans of SFP cable (one per endpoint, spanning each pair of media converters), I can electrically isolate the Ethernet switches (and wired LAN clients connected to them) at each endpoint from any lightning-induced EMI that the external Ethernet spans might pick up. And all for ~$250 total. Thank you! Off to order now…

Transceiver sacrifice

And that’s exactly what I did, initially alluded to in the comments of a teardown (of one of the devices that died in the October 2024 lightning debacle) published the following May. I promised a teardown back then, and although it took me a bit longer than planned to actualize that particular aspiration, you’ll be getting one today.

First off, here’s what one of the four paired TP-Link MC220L Gigabit SFP Media Converter clusters looks like in action, in my furnace room.

One of the only-slightly-quirky devices is Ethernet-fed by the eight-port GbE switch (not shown) next to it. The other one connects to the Ethernet cable that then heads outside and around the west and north sides of the house, where it re-enters at the master bedroom. There’s another two-device cluster there, of course. Two more clusters handle the Ethernet span running between the west and east sides of the house. And interconnecting each two-device cluster is a 0.3 meter strand of SFP fiber optic cable (or so I thought at the time…keep reading).

All nine devices (including a spare) were factory-refurbished, came with multi-year warranties, and cost me less than $20 each (four of them less than $15 each) on eBay. And the cable four-pack from Amazon cost me less than $28. This isn’t a foolproof fix, mind you, but it’s a cost-effective workaround. Even if I need to replace all four external-facing transceivers each time, there’s a monsoon “event”. It’s less than $100 out of pocket (not to mention only a five-minute replacement job), a much less costly outlay than when multiple much more expensive LAN gadgets had gotten fried. In practical preparation, in fact, I’ve already bought six more spares, this time from StarTech (and sourced from Woot) and setting me back only $5 each:

Add fiber to your packet diet

Enough of the background chatter; let’s get to tearing down. The device you’ll be looking at today is not one of the nine TP-Link devices I’ve already mentioned. Nor is it one of the six StarTech ones. It’s a tenth TP-Link MC220L, again from eBay, but this time used and missing a power supply (but still functional? Dunno). I’ll start with a stock shot.

And now some photos of our actual patient, as usual accompanied by a 0.75″ (19.1 mm) diameter U.S. penny for size comparison purposes.

Used, like I said!

No wireless capabilities, thus a rare teardown device absent an FCC certification ID on the label.

Now for the sides (in clockwise order):

Before proceeding further, I grabbed the wall wart and paperwork (PDF) from the spare functional unit, to share some photos of them with you, too.

Protocol conversion here: media conversion elsewhere

I anticipated that getting inside would be relatively straightforward, and I wasn’t disappointed. You probably already noticed the four total screw heads, two each on two of the sides. You know what comes next, right?

And…open sesame:

Two more screws to go:

And the PCB is free:

The design is quite simple; the notable topside contents include a Realtek RTL8367S layer-2 managed 5+2-port 10/100/1000M switch controller and a Group-Tek HST-2027DAR (PDF) dual-port 10/100 BASE-T Ethernet isolation transformer module.

I was initially baffled as to where the optical/wired bidirectional conversion circuitry was located, until I realized that it was at both ends of the cable itself. Unfortunately, I don’t have a spare available to dissect, so you and I will both need to satisfy ourselves with others’ analyses, such as this one, which showcases a module based on an Atheros (now Qualcomm Atheros) AR8033 Ethernet transceiver and two SwapNet NS681679 LAN transformer modules.

And on the other side of the PCB? Nothing but solder points and embedded traces:

I’ll wrap up with a set of side shots:

and turn it over to you for your thoughts in the comments!

Coda

Subsequent to doing the teardown and writing the previous prose, I revisited the SFP cable page at Amazon’s website to purchase another cable for future module teardown purposes and first-time noticed the word “Copper” in the product title. With no shortage of embarrassment, I must admit that the whole time I’d had the media converters active in my network to that point, they’d not been providing any meaningful degree of galvanic isolation after all. I quickly sourced true fiber interconnect, 0.5 meter multimode active optical cables (AOC) to be exact:

and installed them in place of the direct-attach copper (DAC) predecessors I’d been naïvely using up to that point. Although, in my slight defense, I had long been wondering why they’d been so inexpensive. The AOCs, which weren’t that much pricier especially in the ultra-short lengths I needed, work great.

Although in a final twist to this tale, I subsequently learned that (strictly speaking, at least) they shouldn’t be working—at all, actually—since the media converters are SFP-lineage but the cables (and their endpoint transceiver modules) implement the successor SFP+ standard.

That SFP (port)-vs-SFP+ (module) protocol incompatibility exists in contrast to the physical compatibility between SFP and SFP+ connectors and modules is mind-blowing to me. I’m guessing that this mismatch has also caused no shortage of headaches for multi-generation SFP technology suppliers and implementers alike, and that vendors have in response come up with above-and-beyond-the-spec workarounds that support full backwards-compatibility such as the one I thankfully experienced.

I’ll save further discussion for a near-future planned dedicated post on the topic, but felt it was important to do an initial fess-up here.

—Brian Dipert is the associate editor, as well as a contributing editor, at EDN.

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The post TP-Link MC220L: Media conversion keeps the network well appeared first on EDN.

Ерготерапія без кордонів – шведський досвід кафедри біобезпеки і здоров'я людини Інформація КП пн, 06/15/2026 - 14:16

🕔 Дайджест актуальних подій та конкурсів від Відділу академічної мобільності kpi пн, 06/15/2026 - 14:10

NUBURU Inc of Centennial, CO, USA (a dual-use defense & security integrated platform company focused on non-kinetic effects and directed-energy technologies, electronic warfare and defense mobility programs, software-orchestrated defense systems and advanced manufacturing) has signed a binding head of terms with SunCubes S.r.l., an Italian developer of laser-based wireless power transmission, beam-control, pointing, tracking and safety technologies...

In the latest update of KnowMade’s monthly GaN research, technology, and patent landscape monitoring, new scientific breakthroughs, patent activity and industrial announcements confirm that the global gallium nitride (GaN) ecosystem continues to gain momentum as the technology moves deeper into mainstream semiconductor roadmaps, becoming a strategic semiconductor platform across computing, communications, energy and defense applications. The latest monthly R&D activity highlights an industry rapidly evolving beyond its historical positioning in LEDs and RF power devices toward a much broader role spanning AI data centers, optical interconnects, advanced displays, high-voltage power conversion and next-generation communication systems...

China-based Innoscience (Suzhou) Technology Holding Co Ltd, which manufactures GaN-on-silicon power chips on 8” silicon wafers, says that the Supreme People’s Court of China has officially issued a final review decision sustaining a sales injunction against Infineon Technologies AG of Munich, Germany. So, effective immediately, Infineon is prohibited from selling its relevant gallium nitride (GaN) products within mainland China...

In early 2026, a team at Fraunhofer Institute for Solar Energy Systems ISE of Freiburg, Germany working on the project Vorfahrt (Cost Reduction of III–V Cells and Modules for Aircraft and Vehicle Applications) fabricated an 833cm2 photovoltaic (PV) solar module with record energy conversion efficiency of 34.2%. The module consists of triple III–V germanium solar cells originally optimized for space applications, which project coordinator AZUR SPACE Solar Power GmbH of Heilbronn, Germany further developed by adapting them to the terrestrial solar spectrum, enabling them to be produced in comparable quantities and on the same wafer formats...

Surface resistance and resistivity testers are essential tools for evaluating materials used in electrostatic discharge (ESD) control. By quantifying how surfaces resist or conduct electrical charge, they enable engineers to verify compliance with industry standards and safeguard sensitive electronic components.

Because these measurements define whether a material behaves as conductive, dissipative, or insulative, they are central to effective ESD control and protection of high-value electronics.

Surface resistivity vs. surface resistance

It’s easy to confuse surface resistivity testers with surface resistance testers, but in principle they measure different properties. Surface resistivity testers determine a material’s inherent ability to resist charge flow, expressed in ohms per square (Ω/□), and are typically used for material characterization in laboratories.

Surface resistance testers, by contrast, measure the actual resistance between two points or between a surface and ground, expressed in ohms (Ω), making them more common in field audits of ESD workstations, mats, and floors. Recognizing this distinction ensures accurate measurements, proper classification of materials, and effective ESD program control.

In practice, the terms surface resistance and surface resistivity are often used interchangeably in device descriptions because both relate to how materials impede electrical charge across their surfaces. The overlap in measurement setups, industry shorthand, and the focus on ESD compliance ranges (10³–10¹² Ω) all contribute to this blurred usage. What matters most to engineers is whether a material or surface falls within conductive, dissipative, or insulative ranges, not the precise terminology.

This is where surface resistance test kits become especially significant: they provide portable, standardized tools for field audits of ESD workstations, mats, floors, and packaging, ensuring that surfaces meet compliance requirements and offer safe discharge paths for static electricity. By bridging laboratory concepts with real-world checks, these kits make ESD control practical and reliable.

Figure 1 This portable tester—Z203-100—measures surface resistivity and resistance in ESD applications. Source: Zeebeetronics

Sidenote: In ESD protection, surface resistivity (Ω/□) reflects a material’s intrinsic “DNA”—its inherent electrical properties independent of size. Surface resistance (Ω), by contrast, captures “real‑world” performance, shaped by geometry, installation, and grounding. Simply put, resistivity identifies the material; resistance verifies the protection.

The role of probe geometry

Getting again into the distinction between surface resistance and surface resistivity, the technical divergence often comes down to the test probe geometry used during the audit.

In a practical setting, a surface resistance tester is the essential “boots on the ground” tool for verifying if an ESD mat is functional. Unlike lab-based resistivity tests, it measures the actual path a charge takes from point A to point B (or to ground), accounting for real-world variables like surface wear, contamination, and grounding connections. While compact handheld meters are convenient for quick checks, official ANSI/ESD S20.20 audits require the superior accuracy of heavy, “5-pound weight” megohmmeter probes to ensure the environment is truly safe for sensitive electronics.

While a field technician might use two 5-pound weighted electrodes (pucks) to measure the point-to-point resistance of a specific floor or mat, a materials scientist might opt for a concentric ring probe to determine the material’s inherent resistivity.

Because the concentric ring’s circular design ensures the distance between electrodes is mathematically proportional to their size, the units of measurement effectively cancel out, leaving a value in ohms per square. This allows the meter to provide a reading that remains constant regardless of the material’s total surface area, whereas the 5-pound pucks provide a “real-world” measurement of how much resistance a charge actually encounters between two specific points.

Figure 2 Concentric ring probe measuring surface resistance; the geometric constant converts the value to surface resistivity. Source: Desco Europe

A practical pointer: when converting resistance measurements from the concentric ring probe method to equivalent resistivity, multiply the result by the conversion factor specified in the probe’s datasheet. This factor is derived from the specific geometry of the electrode assembly. Note, however, that these conversions may be invalid for non-homogeneous materials, such as those that are laminated, plated, or metallized with conductive layers.

So, while standard 5-pound weighted electrodes are used to measure point-to-point resistance, the concentric ring probe is the gold standard for measuring surface resistivity because its unique geometry—a center electrode surrounded by a circular outer ring—neutralizes surface area variables and orientation. By applying uniform pressure across a fixed distance, this probe allows a resistance tester to calculate true ohms per square (Ω/□), providing a precise material characterization that standard cylinders cannot.

Ultimately, in a professional audit, the 5-pound cylinders verify that the installed mat effectively dissipates charge to ground, while the concentric ring probe confirms that the material itself meets the manufacturer’s specific electrical requirements.

Applied test voltage and electrification period

The applied voltage functions as the electrical pressure that drives current across a material’s surface. On highly conductive surfaces, a 10-V output combined with a brief electrification period (typically around 15 seconds) is sufficient to establish a stable reading without overstressing the sample. As materials shift into dissipative or insulative ranges—where molecular structure resists electron flow—10 V lacks the drive needed to overcome surface impedance.

In these cases, the meter automatically steps up to 100 V, maintaining the same electrification period to ensure the signal penetrates the higher resistance and produces a reliable data point. Without this higher voltage, the instrument could misclassify a dissipative surface as a complete insulator (open circuit). The dual-voltage design, coupled with controlled electrification time, ensures that measurements reflect the material’s true protective properties rather than a limitation of the tester itself.

Note at this point that compliance standards require a 15-second electrification period to ensure stabilized readings. In contrast, many portable field meters are optimized for convenience, displaying results in as little as 2–5 seconds. While suitable for quick checks, these faster readings do not substitute for compliance-grade measurements.

Resistance ranges and material classification

Surface resistance values are categorized into three broad ranges that dictate a material’s electrostatic behavior. Conductive materials (10^3–10^6 Ω) allow charges to move freely, facilitating rapid equalization across the surface. Dissipative materials (10^6–10^11 Ω) provide a controlled pathway that regulates charge decay, preventing the danger of sudden discharge.

Conversely, insulative materials (>10^12 Ω) inhibit electron flow, causing charges to remain trapped on the surface. This framework ensures that test results serve as functional indicators of material performance in sensitive environments.

Maintenance, calibration, and environmental factors

To maintain precise measurements, the electrodes or weighted probes of a surface resistance or resistivity meter must be kept free of contaminants like oils, dust, or skin residue. Cleaning should be performed using a lint-free cloth moistened with 99% isopropyl alcohol, followed by sufficient time to allow the probes to dry completely to prevent solvent-induced measurement errors.

Beyond routine cleaning, periodic calibration—typically on an annual basis—is necessary to verify that the internal circuitry remains within the manufacturer’s specified tolerance using a high-megohm resistance box.

Furthermore, because relative humidity (RH) significantly influences surface resistance by creating a microscopic conductive layer on many materials that can artificially lower readings, it’s critical to always record the ambient RH alongside every measurement for proper context.

Scratching surface, revealing science

That is all for now. Obviously, we just scratched the resistive surface—and much remains hidden in the interplay of surfaces and charge.

In electronics and materials science, surface resistance and resistivity testers are indispensable for gauging reliability, safety, and performance. They help practitioners clearly distinguish between insulating, conductive, and static-dissipative surfaces.

For keen experimenters, building prototypes of such testers does not demand exotic or costly components. With curiosity and patience, the analog and digital design ideas are well within reach. When time permits, I intend to explore these concepts further—and perhaps craft a design of my own.

Now it’s your turn: share your design ideas, prototypes, and experiments—let us advance practical measurements together. Scratch the surface, reveal the science!

T. K. Hareendran is a self-taught electronics enthusiast with a strong passion for innovative circuit design and hands-on technology. He develops both experimental and practical electronic projects, documenting and sharing his work to support fellow tinkerers and learners. Beyond the workbench, he dedicates time to technical writing and hardware evaluations to contribute meaningfully to the maker community.

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The post Surface resistance and resistivity testers for ESD applications appeared first on EDN.