The vast majority of semiconductors products we use every day are primarily constructed on a silicon process, using wafers of pure silicon. But whilst the economics are known, and processes mature, there are still some weaknesses. Especially for power applications. gallium nitride (GaN) and silicon carbide (SiC) are materials that have seen an explosion in uses in the power space, driven especially by an increase in electric vehicle sales and other high-power/high-voltage systems such as solar arrays. But, SiC is expensive and very energy intensive. It looks like diamond substrates could become much more common if the work by Diamfab takes off.
Diamond, specifically thin films of synthetic diamond formed on a suitable substrate, exhibits many desirable properties, such as a vastly superior maximum electric field compared with silicon, and a thermal conductivity five times better than copper. Such properties give diamond structures a big power and voltage advantage over SiC, which is in turn a lot better the pure silicon. This also means that diamond-based transistors are more energy efficient, making them smaller and cheaper, as well as better performing. Without the high formation temperatures needed for SiC, diamond could well be their downfall, especially once you factor in the reduced environmental impact. There is even some talk about solid-state, high-voltage diamond insulator capacitors becoming possible. It certainly is an interesting time to be alive!
AI image generators have gained new tools and techniques for not just creating pictures, but modifying them in consistent and sensible ways, and it seems that every week brings a fascinating new development in this area. One of the latest is Drag Your GAN, presented at SIGGRAPH 2023, and it’s pretty wild.
It provides a point-dragging interface that modifies images based on their implied structure. A picture is worth a thousand words, so this short animation shows what that means. There are plenty more where that came from at the project’s site, so take a few minutes to check it out.
[mat kelcey] was so impressed and inspired by the concept of a very slow movie player (which is the playing of a movie at a slow rate on a kind of DIY photo frame) that he created his own with a high-resolution e-ink display. It shows high definition frames from Alien (1979) at a rate of about one frame every 200 seconds, but a surprising amount of work went into getting a color film intended to look good on a movie screen also look good when displayed on black & white e-ink.
The usual way to display images on a screen that is limited to black or white pixels is dithering, or manipulating relative densities of white and black to give the impression of a much richer image than one might otherwise expect. By itself, a dithering algorithm isn’t a cure-all and [mat] does an excellent job of explaining why, complete with loads of visual examples.
One consideration is the e-ink display itself. With these displays, changing the screen contents is where all the work happens, and it can be a visually imperfect process when it does. A very slow movie player aims to present each frame as cleanly as possible in an artful and stylish way, so rewriting the entire screen for every frame would mean uglier transitions, and that just wouldn’t do.
So the overall challenge [mat] faced was twofold: how to dither a frame in a way that looked great, but also tried to minimize the number of pixels changed from the previous frame? All of a sudden, he had an interesting problem to solve and chose to solve it in an interesting way: training a GAN to generate the dithers, aiming to balance best image quality with minimal pixel change from the previous frame. The results do a great job of delivering quality visuals even when there are sharp changes in scene contrast to deal with. Curious about the code? Here’s the GitHub repository.
Every so often, a new technology comes along that offers a broad range of benefits over what we already have. Just as lithium-ion batteries have made nickel-cadmium cells boring and old hat, gallium nitride semiconductors are making silicon parts look unimpressive by comparison. [Brian Dipert] looked at what this means in a practical sense by tearing down a GaN phone charger.
The charger in question is a 30 watt USB-C charger produced by Voltme. It cost [Brian] just $10, as prices of GaN hardware have come down significantly as economies of scale have kicked in. The charger measures just 1.2×1.3×1.2 inches, and weighs only 1.5 ounces. That compact size is thanks to GaN semiconductors, which are able to run cooler at higher power levels than their silicon forebearers.
Cracking into the charger required levering open the case. The back panel came off with some work, revealing the mains terminals, which deliver AC power to the PCB inside via the case holding them in contact. Interestingly, the entire circuit inside is filled with an adhesive thermal goop, which helps pass heat from the hottest components to the charger’s case. [Brian] is able to guide us through the circuit, and he identified many of the major components. However, some of the markings on chips were beyond his research skills, and he asks any knowing readers to contribute their own information.
It’s interesting to see just what makes the high-powered compact chargers of today tick. Plus, it’s a hallmark of progress that what was once considered a wonder material can now be had in a $10 commodity phone charger from Amazon. How times change!
[Asianometry] has been learning about gallium nitride semiconductors and shares what he knows in an informative video you can see below. This semiconductor material has a much higher bandgap voltage than the more common silicon. This makes it useful for applications that need higher efficiency and less heating.
The original use of the material was for LEDs, but we are seeing increasing use of the material in high-power applications like chargers. Phone chargers are especially common using this technology. This isn’t surprising when your think about how many phone chargers are needed worldwide every day.
Other places that need power-efficient devices are data centers, electric vehicles, and battery-operated equipment. It isn’t clear, though, that we can make enough of the material to meet global demand if it becomes extremely popular. This is especially true because the machinery and processes used to create silicon devices don’t work with gallium nitride. Silicon carbide is a competitor, and it could be easier to create, even though it isn’t as efficient as gallium nitride.
Since the widespread adoption of USB 1.1 in the 90s, USB has become the de facto standard for connecting most peripherals to our everyday computers. The latest revision of the technology has been USB 4, which pushes the data rate capabilities to 40 Gbit/s. This amount of throughput is mindblowing compared to the USB 1.x speeds which were three to four orders of magnitude slower in comparison. But data speeds haven’t been the only thing changing with the USB specifications. The amount of power handling they can do has increased by orders of magnitude as well, as this DIY USB charger demonstrates by delivering around 200 W to multiple devices at once.
The build comes to us from [tobychui] who not only needed USB rapid charging for his devices while on-the-go but also wanted to build the rapid charger himself and for the charger to come in a small form factor while still using silicon components instead of more modern gallium nitride solutions. The solution he came up with was to use a 24 V DC power supply coupled with two regulator modules meant for solar panel installations to deliver a staggering amount of power to several devices at once. The charger is still relatively small, and cost around $30 US dollars to make.
Part of what makes builds like this possible is the USB Power Delivery (PD) standard, which has enabled all kinds of electronics to switch to USB for their power needs rather than getting their power from dedicated, proprietary, and/or low-quality power bricks or wall warts. In fact, you can even use this technology to do things like charge lithium batteries.
The team, comprised of computer scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Microsoft, used paintings from the Metropolitan Museum of Art and Amsterdam’s Rijksmuseum to demonstrate these hidden connections, which link artwork that shares similar styles, such as Francisco de Zurbarán’s The Martyrdom of Saint Serapion (above left) and Jan Asselijn’s The Threatened Swan (above right). They were initially inspired by the “Rembrandt and Velazquez” exhibition in the Rijksmuseum, which demonstrated similarities between the artists’ work despite the former hailing from the Protestant Netherlands and the latter from Catholic Spain.
The algorithm, dubbed “MosAIc”, differs from probabilistic generative adversarial network (GAN)-based projects that generate artwork since it focuses on image retrieval instead. Rather than focusing solely on obvious factors such as color and style, the algorithm also tries to uncover meaning and theme. It does this by constructing a data structure called a conditional k-nearest neighbor (KNN) tree, which provides a tree-like structure where branches off a central image indicate similarity to the image. In order to query the data structure, these branches are followed until the closest match to an image in a dataset is found. In further iterations, it prunes unpromising branches in order to improve its time for new queries.
Some results from running the algorithm against museum collections were finding similarities between the Dutch Double Face Banyan and a Chinese ceramic figurine, traced to the flow of porcelain and iconography from the Chinese to the Dutch in the 16th to 20th centuries.
A surprising result of this study was discovering that the approach could also be applied to find problems with deep nerual networks, which are used for creating deepfakes. While GANs can often have blind spots in their models, struggling to recreate certain classes of photos, MosAIc was able to overcome these shortcomings and accurately reproduce realistic images.
While the team admits that their implementation isn’t the most optimized version of KNN, their main objective was to present a broad conditioning scheme that is simple but effective for applications. Their hope is to inspire related researchers to consider multi-disciplinary applications for algorithms.