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Breast cancer is on the rise, but new tools for early detection could save lives.

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The new software solution can predict e-machine performance, including that of axial flux electric drive units.

Alongside a UK Government Department for Business & Trade mission at the SEMICON Korea event in Seoul (31 January – 2 February), as part of the Government-funded UK–APAC Tech Growth Programme seven British tech companies had 51 introductory business development meetings with South Korean corporations, resulting in several now being engaged in commercial negotiations with potential Korean customers and partners...

Конкурс стартап-проєктів у сфері штучного інтелекту kpi вт, 02/13/2024 - 17:13

День відкритих дверей КПІ ім. Ігоря Сікорського kpi вт, 02/13/2024 - 15:22

A new independent impact evaluation report spanning 2018–2023 undertaken by UK-based full-service research and evaluation agency Harlow Consulting on behalf of Compound Semiconductor Applications (CSA) Catapult finds that CSA Catapult has made significant achievements since its inception in 2018, despite facing many challenges brought about by the COVID-19 pandemic...

Conventional solar cells are just that: photovoltaic devices which, by their physics, extract and transform energy from the sun. Their sensitivity and efficiency are matched to the optical-energy spectrum of radiated and received power from the Sun to the extent possible, Figure 1.

Figure 1 The solar optical spectrum is complex and the available power per wavelength is a function of many factors. Source: Pennsylvania State University

In many small-scale applications, these same solar cells are used indoors and powered by ambient light from source’s overhead fixtures (which may be fluorescent or LEDs of various color temperatures), incandescent lamps (yes, some are still out there), diffuse or shaded natural light, and even specialized light such as halogen sources.

Given the indoor situation, two things are obvious:

• The designation as “solar” is somewhat of a misnomer since the Sun is no longer the source and so “photovoltaic” (PV) would be more accurate—but that’s the widely used, colloquial way of describing these cells.
• The energy spectrum of these indoor lighting sources is mismatched to the responsiveness of the solar cells, so efficiency is low.

While there have been some smaller, less-critical indoor products using solar power alone such as small calculators, such harvesting of ambient indoor optical energy is generally limited in its usefulness.

That situation may be changing as several companies have developed solar cells (we’ll stick with that misnomer) based on technologies which are very different from those used by conventional “real” solar cells. These indoor-optimized cells use complex layers of dyes along with specialized physical and chemical processes to achieve their indoor-optimized results.

Both Ambient Photonics (Scotts Valley, CA) and Exeger Operations AB (Stockholm) use variations of dye sensitized solar cell (shortened to DSC or DSSC) technology to produce light-sensitive cells which are optimized for indoor settings. The production process is a high-volume printing-like operation rather than the furnace-based process used for conventional solar cells.

Ambient says they have reinvented the chemistry of the dye sensitized solar cell (DSSC) with novel, proprietary molecules, using light-sensitive dyes to collect photons and convert them into electrons. In their electrochemical system, these light-sensitive dye molecules harvest and produce energy, with the dyes functioning similar to how chlorophyll behaves during photosynthesis in converting photons into energy.

They maintain that their energy-harvesting technology can harness photons across the light spectrum, yielding more than 90 percent conversion efficiency in low-light condition, even when compared to standard DSSC cells, Figure 2. They also function effectively despite the dynamic, changing indoor low-light conditions which are largely a function of the time of day.

Figure 2 Ambient says their DSSC process yields results which are superior to conventional film-based PV cells. Source: Ambient Photonics

Exeger’s dye sensitized solar cell uses a new architecture which they say improves real-life performance, provides greater flexibility, and offers seamless integration possibilities. In their approach, a unique conductive electrode material has replaced the traditional expensive and inefficient indium-tin-oxide (ITO) layer, Figure 3.

Figure 3 The Exeger’s process requires multiple layers of sophisticated materials and films and is compatible with mass production. Source: Exeger Operations AB

Dubbed Powerfoyle, it is flexible and durable and so can be integrated on curved surfaces such as headbands, Figure 4. It can be produced in sizes from 15 cm² to 500 cm², and therefore integrated into products ranging from small IoT sensors to speakers and larger accessories.

Figure 4 A bendable, flexible solar cell opens up new design-in and application opportunities. Source: Exeger Operations AB

For most design engineers, how these companies have achieved their indoor-friendly solar cells is not as important as what these innovations may do with respect to design options and degrees of freedom. Do power sources such as these enable increased consideration of IoT devices (sensors, trackers, shelf labels, and even remote controls) which do not need battery replacement, yet require more power other harvesting schemes (such as ambient RF-harvesting) support? For example, electronic door locks in hotels are an interesting possibility, as they are continually exposed to indoor lighting and used relatively infrequently; in theory, that’s a good combination of harvesting and use cycles.

Applications do not have to be limited to such small devices, either; Exeger has an agreement with a headphone manufacturer for ambient-powered units with the headband capturing ambient light. The same idea can be used for providing power to safety vests and alarm devices.

Of course, the energy source itself is only part of the harvesting chain. For designers, the dominant issue is not “how did they do it” but instead “what can it perhaps do for me?”; “what new opportunities does it provide?”; and “what do I need to do in my design to make use of this power source?”

For example, designers will have to decide on a suitable energy-storage and charging arrangement, whether using a rechargeable battery and the issues of limits on viable charge/discharge cycles, or a supercapacitor and the unique issues of using these non-chemical storage cells.

It will be interesting to see if these indoor-friendly solar cells become a standard part of the design-in possibilities, or if they have downsides which only become apparent when you get into the nitty-gritty design details of product design, manufacturing, use patterns, and long-term performance. 

Do you see a viable energy-harvesting role for these non-Sun-driven solar cells? Would they allow you to create something you haven’t been able to do thus far? What possible design-in issues do you see?

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

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• Are solar-powered cars the ultimate electric vehicles?
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• Enough With the Solar Power ‘Magic,’ Please!
• Solar Energy Harvesting: A Great Solution, Except When It’s Not

The post Indoor solar cells spur design-option reassessments appeared first on EDN.

BluGlass Ltd of Silverwater, Australia — which develops and manufactures gallium nitride (GaN) blue laser diodes based on its proprietary low-temperature, low-hydrogen remote-plasma chemical vapor deposition (RPCVD) technology — has received $4.3m in commitments from institutional and sophisticated investors via a share placement at an issue price of $0.037 per share...

Ректорат 12 лютого medialab вт, 02/13/2024 - 09:46

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In this roundup, we highlight three new DC-DC converters that emerged in the last few months, each driven by package and performance improvements.

Звіт ректора за 2023 рік M.Zgurovsky вт, 02/13/2024 - 00:34

Matter-over-Thread applications are now more accessible to Arduino's developer community of 33 million.

Пам'ятаймо політехніків, загиблих у війні з росією Інформація КП пн, 02/12/2024 - 18:20

Chip and integrated module supplier Sivers Semiconductors AB of Kista, Sweden says that its subsidiary Sivers Semiconductors Inc has appointed Dr Bami Bastani as US executive chairman, and that he has also been nominated as the chairman of the board of directors of Sivers Semiconductors AB...

The Optical Internetworking Forum (OIF) says that at the Optical Fiber Communication Conference and Exposition (OFC 2024) in San Diego, CA, USA (24-28 March) a record 47 member companies are showcasing the interoperable solutions that are addressing the unprecedented demand for next-generation data-center networking, artificial intelligence/machine learning (AI/ML) and disaggregation applications...

For its fiscal second-quarter 2024 (to 30 December 2023), Lumentum Holdings Inc of San Jose, CA, USA has reported revenue of $366.8m, down 27.5% on $506m a year ago but up 15.5% on $317.6m last quarter, and above the midpoint of the $350–380m guidance. However, $59.5m of this came from a partial quarter (about eight weeks) of revenue after the acquisition on 7 November of Cloud Light Technology Ltd of Hong Kong (which designs and makes optical transceiver modules for automotive sensors and data-center interconnect applications), mostly from very strong shipments of 800G transceivers for artificial intelligence (AI) platforms...

The personal computer (PC) industry is warming up to a new sweet spot: PCs incorporating artificial intelligence (AI) capabilities. Intel and Microsoft—the primary beneficiaries of the PC revolution—are now pushing PCs with AI-enabled CPUs and AI-powered software assistants, respectively, to move AI applications from the cloud to the PC realm.

In other words, AI PCs embedded with specialized chips can run AI models locally without relying on the cloud. That, according to Intel CEO Pat Gelsinger, will make AI services cheaper, faster, and more private than using services based in cloud-centric data centers. “You’re unleashing this power for every person, every use case, every location in the future,” he said at the CES 2024 in Las Vegas.

Intel, while competing with AI powerhouse Nvidia in server space, clearly sees an opportunity to catch up in its forte: PC processors. What it’s doing right now is integrating neural processor units (NPUs) into PC processors; NPUs are specialized semiconductors dedicated to handling AI tasks.

Intel’s Meteor Lake laptop CPU has incorporated an NPU to support third-party AI software features. Its archrival in the PC hardware space, AMD, has also been shipping AI PC processors. Next, Nvidia showcased three new GPUs—RTX 4060 Super, RTX 4070 Ti Super, and RTX 4080 Super—for AI-ready laptops at a virtual event before CES 2024.

Figure 1 Meteor Lake CPU has incorporated an NPU to support AI applications. Source: Intel

Besides AI-ready processors, memory chipmakers like Micron, Samsung, and SK hynix are also eyeing AI PCs to enable AI accelerators to run powerful assistants on personal computers. New laptops currently come with as much as 8 MB of RAM, and it’s likely to double in Windows-based AI PCs. In fact, a large language model (LLM) running an AI assistant could require more than 16 MB of memory.

Take the example of the Llama 2 family of AI models created by Meta, which requires nearly 30 GB of RAM for its modest variant. Moreover, the amount of memory in AI PCs will likely increase with the availability of more powerful AI accelerators and processors.

Figure 2 Copilot AI assistant is built around OpenAI’s GPT-4 model. Source: Microsoft

At the moment, consumers are only warming up to AI personal computers, and it’ll take a while before more native applications are made available for AI PCs. However, both hardware and software for AI PCs will become more powerful over time, and that’s good news for semiconductor devices like CPUs, GPUs, and DRAMs.

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The post Will AI PCs be a new sweet spot for CPUs and DRAMs? appeared first on EDN.

Persistence displays retain waveform traces on the screen, allowing them to decay over a user set time duration, they allow users to see a history of signal variations on the screen. This feature is very useful if you are adjusting a signal, as it allows you to see the changes as they are made. Some oscilloscope applications require displaying a history of events in order to see how the signal varies over time. Persistence displays are key tools for viewing such signal changes as a function of time over multiple acquisitions. The most common applications that use persistence displays include jitter analysis of a serial data transmission and eye diagrams used for digital communications systems (Figure 1).

Figure 1 The persistence display of timing jitter on an edge. Multiple acquisitions are retained on the display of the edge to show the variation in its timing. Source Arthur Pini

 This is an analog persistence view of jitter on a clock edge, it is a monochrome display where the brighter areas are the more often occurring signal paths and the duller areas occur less often. The center area of the transition is brighter, meaning more edges pass at that time than during the times corresponding to the outer edges.

The same data can be viewed in color-graded persistence, a tool used to map the frequency of occurrence spectrally. Most frequent events appear in red while the least frequent events are shown in violet (Figure 2).

Figure 2 A color graded persistence display of the same edge jitter. The red areas occur more often than violet areas. Source Arthur Pini

The intermediate frequency of occurrence is mapped spectrally, from most to least often occurring as red-orange-yellow-green-blue-indigo-violet.

Multiple acquisitions are acquired and stored in a persistence map which shows signal variations over time. The persistence decay time is user-selectable with a time constant from half a second to infinite. A saturation control allows users to control the mapping of frequency of occurrence to intensity or color. 

Eye and state transition diagrams

Persistence displays also help analyze data communications signals, where they are used to display eye diagrams and state transition diagrams (Figure 3).

Figure 3 The eye diagrams of the I and Q components and state transition diagrams of a 16-QAM signal rendered in monochrome analog persistence. Source: Arthur Pini

The eye diagrams of a 16-QAM signal show the results of 12,890 acquisitions of the I and Q signal components, which are also cross plotted as an X-Y plot, forming the state transition diagram shown in the upper right corner. Again, the intensity variations are proportional to the amount of time a waveform falls on a particular point on the display. The highly repetitive elements of a signal are brighter than the rarely occurring signal events. The data states, which appear as horizontal lines in the I and Q traces, are written more often and show up brighter than the transitions, which take different paths and occur with less frequency at any given point. The same is true of the state transition diagram where the data states appear as bright dots and the transition paths have a lower intensity.

Persistence histograms

All the data behind the persistence display is available and can be used to quantify the acquired data statistically. One example is to generate a histogram from the persistence display. The oscilloscope used in this article has a function called persistence histogram, it lets the user define either a horizontal or vertical slice through the

persistence display and then forms a histogram as shown in Figure 4.

Figure 4 A persistence histogram with a horizontal slice of the jitter persistence display centered at a level of 0 mV with a width of 10 mV. Source: Arthur Pini

The persistence histogram appears in the trace below the persistence display. Cursors are used to mark the location where the histogram slice originates. In a vertical slice, each bin of the histogram contains a class of related amplitude levels. A horizontal slice, used in the example, produces a histogram where each bin contains a class of related time values.

In the example, the vertical axis of the histogram reads the number of times a specific horizontal pixel is hit. The peak of the histogram corresponds to the central area with a light blue color, while the falling sides correspond to the persistence display changing from indigo to violet. The histogram can be measured using the oscilloscope’s measurement parameters, the measurement parameters P1 through P3 beneath the display grids read the mean, the standard deviation, and the range of the histogram. Parameter help markers annotate the locations of these measurements on the histogram itself.

Persistence histograms can also be applied to eye diagrams showing the horizontal timing uncertainty as well as the vertical deviation (Figure 5).

Figure 5 Application of persistence histogram to an eye diagram permits analysis of noise and jitter on the eye. Source: Arthur Pini

The histogram in the center trace was taken from a horizontal slice through the eye crossing and shows the range of variation in the time of the crossings. The lower histogram was taken using a vertical slice centered between the crossings, it shows the uncertainty in the amplitude of the eye in the center. Some oscilloscopes may not offer measurements that quantify eye characteristics such as eye height and width, .these can actually be obtained using persistence histograms and their associated statistical measurements.

 Persistence trace functions

Persistence trace functions take the histogram of the persistence values over a number of vertical slices set by the user and extract the mean, standard deviation, and range of the persistence data at each slice. It then plots the extracted statistical parameter over time (Figure 6).

Figure 6 Examples of the persistence trace mean (second from the top), persistence trace sigma (third from the top), and persistence trace range (bottom) traces. Source: Arthur Pin)

The persistence trace mean function plots the mean value of the histograms at each of the user’s selected intervals. The resultant plot is the average value of the source persistence trace. In this example, the trace is taken from one thousand points along the persistence trace. This function shows the underlying waveform without vertical noise. Persistence trace sigma plots the minimum and maximum values of the standard deviation about the mean using an extrema plot. The plot shows mean + and – one standard deviation. This function provides a view of the rms noise on the source waveform. The persistence trace range plots the minimum and maximum values of the persistence histogram about the mean and shows the range of the histogram. It is the worst-case range of possible values, especially noise, at each point.

Persistence trace mean is the most useful of the functions allowing a quick determination of the average value of a persistence trace. It is also useful to smooth out traces acquired with low sample point counts (Figure 7).

Figure 7 The persistence trace mean shows all the possible states in waveform with a low sample count by retaining multiple acquisitions. Source: Arthur Pini

Waveforms with low sample counts, displayed with linear interpolation, may appear angular and discontinuous however they are not, and over multiple acquisitions, they trace a smooth waveform. Using persistence trace mean to view the waveform allows the persistence history to fill in the intermediate states and smooth the waveform, showing its actual structure.

3-D persistence display

Adding vertical height to a persistence display proportional to the rate of occurrence gives you a three-dimensional (3-D) effect. This 3-D persistence display creates a topographical view of your waveform.

As shown in Figure 8, this is most useful when studying X-Y plots of signals such as QPSK.

Figure 8 The in-phase and quadrature components of a QSPK signal and a three-dimensional persistence plot of a QPSK state transition diagram. Source: Arthur Pini

The three-dimensional plot retains the color or intensity coding of the persistence displays but adds height proportional to the frequency of occurrence of the display pixels. The shape of these peaks provides an alternative view of the frequency of occurrences in your signals. In this example, the data states of the signal which occur most frequently appear as the highest elements in the X-Y display and are coded in red. Transition paths have more variation and occur less repetitively. They are lower on the display and coded in yellow/green. Off path regions are at the bottom of the display, coded in violet. Controls allow for rotating the 3-D plot to view it from different angles.

The 3-D display can be rendered in three different qualities. The first is as a solid, as is shown, and is the default quality. It can also be rendered in the wireframe quality; this is constructed using lines of equal intensity to create the persistence map. The third quality is shaded, which is only available in monochrome persistence. Shaded quality shows the 3-D object as if it were illuminated by projected light, the shading emphasizes the shape of the object.

The value of persistence displays

Whether used to measure jitter, eye diagrams, or state transition diagrams, persistence is a valuable display technique. When combined with math persistence analysis tools and related measurements, it becomes a powerful tool for quantifying signal variations.

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

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The post Oscilloscope persistence displays appeared first on EDN.