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Vishay Intertechnology Inc of Malvern, PA, USA has introduced new blue and true green surface-mount LEDs in the ultra-compact MiniLED package. Measuring 2.2mm x 1.3mm x 1.4mm, the Vishay Semiconductors VLMB2332T1U2-08 blue and VLMTG2332ABCA-08 true green LEDs utilize the latest ultra-bright indium gallium nitride (InGaN)-on-sapphire chip technology to achieve typical luminous intensities at 20mA of 440mcd and 2300mcd, respectively, which is up to four times higher than previous-generation solutions in PLCC-2 packages...

Singulus Technologies AG of Kahl am Main, Germany (which makes production equipment for the optical disc and solar sectors) has introduced the TIMARIS STM modular high-vacuum sputtering system, which is said to offer significant progress in micro-LED manufacturing. The first TIMARIS STM system has already been sold and delivered...

A new electron paramagnetic resonance (EPR) spectrometer aims to open the technology to a larger pool of scientists by making it cheaper, lighter, and easier to use without needing an experienced operator. Its control software—designed to be intuitive with several automated features—makes the set-up straightforward and doesn’t require an expert in EPR spectroscopy to obtain results.

EPR or electron spin resonance (ESR) spectroscopy, while quite similar to nuclear magnetic resonance (NMR) spectroscopy, examines the nature of unpaired electrons instead of nuclei such as protons. It’s commonly used in chemistry, biology, material science, and physics to study the electronic structure of metal complexes or organic radicals.

Figure 1 The new EPR spectrometer is modular in design and is smaller, lighter, and cheaper than traditional solutions. Source: Spectrum Instrumentation

However, EPR spectrometers are commonly built around massive electromagnets, so they can weigh over a ton and are usually placed in basements. Bridge12, a startup located near Boston, Massachusetts, claims to have produced an EPR spectrometer that is about half the cost of current instruments and a tenth of the size and weight so that it can be placed on any floor of a building (Figure 1).

The new EPR spectrometer is built around two basic building blocks: an arbitrary waveform generator (AWG) to generate the pulses and a digitizer to capture the returning signal. These building blocks are implemented as cards supplied by German firm Spectrum Instrumentation, making the design modular and flexible for end users.

First, an AWG generates 10 to 100-ns long pulses in the 200 to 500 MHz range as required by the experiment, which are then first up-converted to 10-GHz X band range using an RF I/Q mixer and then up-converted to the Q band range. The microwave pulses are then fed into a 100-W solid-state amplifier before being sent to the EPR resonator.

Next, the reflected signal is down-converted to an IF frequency in the 200 to 500 MHz range and sent to the digitizer. Unlike the traditional EPR spectroscopy, where the signal is down-converted to DC, this new approach drastically reduces noise and artifacts.

Figure 2 shows an example of AWG-generated pulses used in an EPR experiment. See WURST (Wideband, Uniform Rate, Smooth Truncation) pulses, which are broadband microwave pulses with an excitation bandwidth and profile that exceeds that of a simple rectangular pulse by far. These pulses facilitate broadband excitation in EPR spectroscopy while heavily relying on the performance of the AWG.

Figure 2 The AWG-generated WURST pulses are displayed in an EPR spectroscopy experiment. Source: Spectrum Instrumentation

The modular design of this EPR spectrometer built around AWG and digitizer cards is integrated into Netboxes, which can be connected to a PC through Ethernet. So, a compact PC can replace a system that is big enough to insert cards, which inevitably leads to a bulky rack solution. As a result, it’s much easier to service EPR spectrometer and replace components in the field.

Another noteworthy design feature of this new EPR spectrometer relates to the much smaller, super-conducting magnet to produce the required magnetic field strength. EPR spectrometers usually use huge, heavy electromagnets to generate intense magnetic fields in the order of 1 to 1.5 Tesla.

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The post EPR spectrometer and its AWG and digitizer building blocks appeared first on EDN.

At Embedded World 2024, STMicroelectronics showed off a 4 mm x 4 mm NFC reader that balances performance, cost, and power consumption.

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My bench desk submitted by /u/Part_salvager616 [link] [comments]

Bench submitted by /u/Part_salvager616 [link] [comments]

We analyze a Ruthroff 1:4 transformer's performance at high frequencies, then learn how to improve its bandwidth using equal-delay networks and how to redesign the circuit for higher impedance transformation ratios.

A collaboration between TriEye Ltd of Tel Aviv, Israel and Vertilas GmbH of Garching bei München, Germany has led to the development of a joint technology demonstrator that integrates TriEye’s Raven short-wave infrared (SWIR) image sensor with Vertilas’ indium phosphide (InP) vertical-cavity surface-emitting laser (VCSEL) technology. Adopting high-volume, scalable manufacturing strategies, the technologies are said to provide cost-effective solutions for both consumer and industrial markets...

Automotive ethernet is increasingly utilized for in-vehicle electronics to transmit high speed serial data between interconnected devices and components. Due to the relatively fast data rates, and the complexity and variation of the networked devices, signal integrity issues can often arise. This article outlines several real-world challenges and provides insight into how to identify and debug automotive ethernet physical layer signal integrity problems using an oscilloscope. The following is a case study of automotive ethernet debugging performed at Inspectron, a company that designs and manufactures borescopes, embedded Linux systems, and camera inspection tools.

Automotive ethernet hardware debug configuration

The automotive ethernet signal path is bi-directional (full duplex on a single twisted pair), so hardware transceivers must be able to discern incoming data by subtracting their own outbound data contributions from the composite signal. If one were to directly probe an automotive ethernet data line, a jumbled superposition resembling a bus collision would be acquired. To make sense of the individual signals being sent, bi-directional couplers can be used.

Figure 1 shows the hardware configuration used to debug an automotive ethernet setup. The two automotive ethernet devices under test (DUTs) are a ROCAM mini-HD display and a Raspberry Pi (with a 100Base-TX to 100Base-T1 bridge). The Raspberry Pi is used to simulate an ethernet camera. The twisted pairs from the DUTs are attached to adapter boards which break out the single 100 Ω differential pair into two 50 Ω single-ended SMA connectors. Each DUT has its pair of SMA cables connected to a calibrated active breakout fixture (Teledyne LeCroy TF-AUTO-ENET). The breakout fixture maintains an uninterrupted communication link, while two calibrated and software-enhanced hardware directional couplers tap off the traffic from each direction into separate streams which isolate the automotive ethernet traffic from each direction for analysis on the oscilloscope.

Figure 1 (a) The hardware configuration used to debug an automotive ethernet setup involves two DUTs, passive fixtures to adapt from automotive ethernet to SMA, and a calibrated active breakout fixture with bi-directional couplers to isolate traffic from each direction. The oscilloscope will analyze both upstream and downstream traffic. (b) The block diagram of the test setup. Source: Teledyne LeCroy

Identifying where signal loss occurs

Intermittent signal loss occurred between the ROCAM mini-HD display and the Raspberry Pi. One method to capture an intermittent loss of data transmission is a hardware Dropout trigger. In Figure 2, a Dropout trigger is armed to trigger the oscilloscope if no signal edge crosses the threshold voltage within 200 nanoseconds (ns). The two Zoom traces scaled at 200 ns/div show the trigger point one division to the right of the previous automotive ethernet edge. A loss of signal occurred for approximately 800 ns before data transmission recommenced. Note that since automotive ethernet 100Base-T1 is a three-intensity level (+1, 0, -1) PAM3 signal, the eye pattern with over 192,000 bits in the eye still shows good signal integrity (data dropout blends in with “0” symbols), but the Zoom traces at the Dropout trigger location reveal the location of signal loss.

Figure 2 The eye pattern shows a clean automotive ethernet 100Base-T1 signal, while the Dropout trigger identifies and locates a signal loss event. Source: Teledyne LeCroy

Amplitude modulation of serial data

Anomalous amplitude modulation or baseline wander issues can often be caught by triggering at a high threshold, slightly above the logic +1 voltage level (for the non-inverting input from the split differential signal). Intermittent anomalous amplitude modulation occurred on the automotive ethernet signal, and an instance was captured with the edge trigger set slightly above the highest expected voltage level, shown in Figure 3. The red histogram with three peaks, taken from a vertical slice through the eye diagram in the center of the symbol slot, show an asymmetry in the statistical distribution of the lowest and highest of the three voltage levels; this is due to the intermittent anomalous amplitude modulation of the signal. There is also an asymmetry of the eye width between the upper and lower eyes, identified in the eye measurement parameter table below the waveforms.

Figure 3 The three red histograms in the lower right grid show an asymmetry in the eye pattern due to intermittent anomalous amplitude modulation. The edge trigger raised to a high voltage threshold, catches an instance of the anomalous amplitude modulation. Source: Teledyne LeCroy

Intermittent amplitude reduction of signal

During the debug process, a malfunction was detected in which the amplitude of the signal would drop to 50% of the expected level. This problem was initially detected with the eye pattern, in which there was a collapse of the eye. In order to detect the location in time where the problem occurred, a dropout trigger was set with a threshold level at approximately 80% of the amplitude of the automotive ethernet signal. When the signal dropped to half amplitude, the Dropout trigger caught the event, showing the amplitude reduction at the point of occurrence. Zoom traces superimposed over the original waveform captures shows poor signal integrity in the time domain traces, which is also indicated in the collapsed eye.

Figure 4 The location of occurrence of the automotive ethernet amplitude reduction is caught using the Dropout trigger with a threshold set to approximately 80% of the waveform amplitude. The poor signal integrity of the reduced amplitude signal is shown in both the eye pattern and in the time synchronized Zoom traces. Source: Teledyne LeCroy

Addressing real-world automotive ethernet scenarios

Physical layer problems in automotive ethernet designs can be elusive and difficult to detect. This article outlined several real-world scenarios which occurred during the implementation of an automotive ethernet network with specific techniques used to identify each type of problem and where in time it occurred. This was accomplished using a combination of triggering, Zooms, eye patterns, statistical distributions, and measurement parameters.

Dave Van Kainen is a Founding Partner of Superior Measurement Solutions and holds a BSEE from Lawrence Tech.

Mike Hertz is a Field Applications Engineer at Teledyne LeCroy and holds a BSEE from Iowa State and an MSEE from Univ. Arizona.

Patrick Caputo is Chief Product Architect at Inspectron, Inc., and holds dual BSs in EE and Physics and an MS in ECE from Georgia Tech.

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The post Practical tips for automotive ethernet physical layer debug appeared first on EDN.

Infineon Technologies AG of Munich, Germany is supplying its power semiconductor devices to inverter and energy storage system maker FOXESS of Shanghai, China (founded in 2019). Specifically, Infineon will provide CoolSiC MOSFETs 1200V, which will be used with EiceDRIVER gate drivers for industrial energy storage applications. At the same time, FOXESS’ string photovoltaic (PV) inverters will use Infineon’s IGBT7 H7 1200V power semiconductor devices...

Засідання організаційного комітету з проведення виборів ректора [16.04.2024] kpi ср, 04/17/2024 - 16:35

Microchip made an important addition to its family of USB MCUs with the new security-conscious AVR DU family.

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Next I want to look into making some registers, and maybe solder some cable with pins to act as a bus. Also want to buy smaller resistors, they are HUGE submitted by /u/josufh [link] [comments]

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Radar is cropping up everywhere in new car designs: sensing around the car to detect hazards and feed into decision making for braking, steering, and parking and in the cabin for driver and occupancy monitoring systems. Effective under all weather conditions, now high-definition radar can front-end AI-based object detection, complementing other sensor channels to further enhance accuracy and safety.

There’s plenty of potential for builders of high value embedded radar systems. However, competitively exploiting that potential can be challenging. Here we explore some of those challenges.

Full system challenges

Automotive OEMs aren’t simply adding more electronic features to new vehicles; they are driving unified system architectures for their product lines to manage cost, simplify software development and maintenance, and enhance safety and security.

So, more compute and intelligence are moving into consolidated zonal controllers, communicating on one side between relatively small sensor units and processors within a small zone of the car, and on the other side, between zonal controllers and a central controller, managing overall decision making.

Suppliers aiming at automotive radar system markets must track their solution architectures with these changes, providing scalability between relatively simple processing for edge functions and more extensive capability for zonal or central controllers, while being flexible to adapt to different OEM partitioning choices.

One important implication is that however a solution might be partitioned, it must allow for significant amounts of data to be exchanged between edge, zonal, and central compute. Which raises the importance of data compression during transmission to manage latency and power.

In addition to performance, power and cost constraints, automotive systems must also factor in longevity and reliability. The full lifetime of a car may be 10, 20 or more years during which time software and AI model upgrades may be required to fix detected problems or to meet changing regulatory requirements.

Those constraints dictate a careful balance in radar system design between the performance/low power of hardware and the flexibility of software to adapt to changes. Nothing new there, but radar pipelines present some unique demands when compared to vision pipelines.

Pipeline challenges

A full radar system flow is shown in the figure below, from transmit and receive antennae all the way to target tracking and classification. Antennae configurations may run from 4×4 (Tx/Rx) for low-end detection up to 48×64 for high-definition radars. In the system pipeline following the radar front-end are FFTs for computing first range information and then Doppler information. Next is a digital beamforming stage to manage digital streams from multiple radar antennae.

A complete radar system pipeline spans from transmit/receive antennae all the way to target tracking and classification. Source: Ceva

Up to this point, data is still somewhat a “raw signal”. A constant false alarm rate (CFAR) stage is the first step in separating real targets from noise. Angle of Arrival (AoA) calculations complete positioning a target in 3D space, with Doppler velocity calculation adding a 4th dimension. The pipeline rounds out with target tracking, using for example an Extended Kalman Filter (EKF), and object classification typically using an OEM-defined AI model.

OK, that’s a lot of steps, but what makes these complex? First, the radar system must support significant parallelism in the front-end to handle large antennae arrays pushing multiple image streams simultaneously through the pipeline while delivering throughput of between 25 and 50 frames per second.

Data volumes aren’t just governed by the number of antennae. These feed multiple FFTs, each of which can be quite large, up to 1K bins. Those conversions stream data ultimately to a point cloud, and the point cloud itself can easily run to half a megabyte.

Clever memory management is critical to maximizing throughput. Take the range and Doppler FFT stages. Data written to memory from the range FFT is 1-dimensional, written row-wise. The Doppler FFT needs to access this data column-wise; without special support, the address jumps implied by column accesses require many burst-reads per column, dramatically dropping feasible frame rates.

CFAR is another challenge. There are multiple algorithms for CFAR, some easier to implement than others. The state-of-the-art option today is OS-CFAR—or ordered statistics CFAR—which is especially strong when there are multiple targets (common for auto radar applications). Unfortunately, OS-CFAR is also the most difficult algorithm to implement, requiring statistics analysis in addition to linear analysis. Nevertheless, a truly competitive radar system today should be using OS-CFAR.

In the tracking stage, both location and velocity are important. Each of these is 3-dimensional (X,Y,Z for location and Vx,Vy,Vz for velocity). Some EKF algorithms drop a dimension, typically elevation, to simplify the problem; this is known as 4D EKF. In contrast, a high-quality algorithm will use all 6 dimensions (6D EKF). A major consideration for any EKF algorithm is how many targets it can track.

While aircraft may only need to track a few targets, high-end automotive radars are now able to track thousands of targets. That’s worth remembering when considering architectures for high-end and (somewhat scaled down) mid-range radar systems.

Any challenges in the classification stage are AI-model centric, so not in range of this radar system discussion. These AI models will typically run on a dedicated NPU.

Implementation challenges

An obvious question is what kind of platform will best serve all these radar system needs? It must be very strong at signal processing and must meet throughput goals (25-50 fps) at low power, while also being software programmable for adaptability over a long lifetime. That argues for a DSP.

However, it also must handle many simultaneous input streams, arguing for a high degree of parallelism. Some DSP architectures support parallel cores, but the number of cores needed may be overkill for many of the signal processing functions (FFTs for example), where hardware accelerators may be more appropriate.

At the same time, the solution must be scalable across zonal car architectures: a low-end system for edge applications, feeding a higher end system in zonal or central applications. It should provide a common product architecture for each application and common software stack, while being simply scalable to fit each level from the edge to the central controller.

Tomer Yablonka is director of cellular technology at Ceva’s mobile broadband business unit.

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The post Challenges in designing automotive radar systems appeared first on EDN.

Micron simplifies data access in new vehicle architectures with its first multi-port automotive SSD.

Стартував 10-й сезон стартап-школи Sikorsky Challenge medialab вт, 04/16/2024 - 20:54

Independent deep-tech venture capital fund PhotonVentures says that its second fundraising round of over €15m has been completed, bringing the fund’s total to the targeted €75m, following the first €60m round led by PhotonDelta and private investors...