This October, I left the comfortable embrace of Earth’s gravity, taking to the skies aboard a “zero-gravity flight.”
A generation after a NASA spacecraft’s probe found an unexpectedly hot and dense atmosphere at Jupiter, a newer agency mission may have some answers to the puzzle.
NASA’s Juno spacecraft discovered that these “hot spots” on the gas giant planet — which the Galileo spacecraft discovered in 1995 — are wider and deeper than previous models and observations suggest, according to results revealed Dec. 11 at the American Geophysical Union’s annual fall conference, held virtually this year due to the coronavirus pandemic.
One of the ongoing questions these past few months has been why so many tech products have been so hard to buy. We’ve made repeated reference to known potential factors like COVID-19, economic disruptions, yield issues, and the impact of scalping bots, but there’s a new argument for what’s causing such general problems across so many markets: Insufficient investment in 200mm wafers.
Today, leading-edge silicon is invariably manufactured on 300mm wafers. Over the past few decades, manufacturers have introduced larger wafer sizes: 100mm, 150mm, 200mm, and 300mm have all been common standards at one time or another. In the PC enthusiast space, 300mm wafers have long been considered superior to 200mm wafers, because the larger wafer size reduces waste and typically improves the foundry’s output in terms of chips manufactured per day.
There aren’t that many commercial foundries still dedicated to 150mm or smaller wafer sizes, but a number of foundries still run 200mm fab lines. TSMC and Samsung both offer the node, as well as a number of second-tier foundries. GlobalFoundries has 200mm facilities, as do SMIC, UMC, TowerJazz, and SkyWater. A great many IoT and 5G chips are built on 200mm, as are some analog processors, MEMS devices, and RF solutions.
I’m really excited to announce my new book And today the Kindle ebook version is FREE instead of $7.99. Richard Dawkins has shared some of the essays in this book in his social media before. Please download a FREE copy and share with friends and family! It has some of my new work in it!
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Fears loom over the discovery of a new variant of COVID-19. What do we know so far?
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Even Apple wants to get into the automobile business it seems.
(Reuters) — Apple Inc is moving forward with self-driving car technology and is targeting 2024 to produce a passenger vehicle that could include its own breakthrough battery technology, people familiar with the matter told Reuters.
The iPhone maker’s automotive efforts, known as Project Titan, have proceeded unevenly since 2014 when it first started to design its own vehicle from scratch. At one point, Apple drew back the effort to focus on software and reassessed its goals. Doug Field, an Apple veteran who had worked at Tesla Inc, returned to oversee the project in 2018 and laid off 190 people from the team in 2019.
Since then, Apple has progressed enough that it now aims to build a vehicle for consumers, two people familiar with the effort said, asking not to be named because Apple’s plans are not public. Apple’s goal of building a personal vehicle for the mass market contrasts with rivals such as Alphabet Inc’s Waymo, which has built robo-taxis to carry passengers for a driverless ride-hailing service.
An especially counter-intuitive feature of quantum mechanics is that a single event can exist in a state of superposition—happening both here and there, or both today and tomorrow.
Such superpositions are hard to create, as they are destroyed if any kind of information about the place and time of the event leaks into the surrounding—and even if nobody actually records this information. But when superpositions do occur, they lead to observations that are very different from that of classical physics, raising questions that spill over into our very understanding of space and time.
Scientists from EPFL, MIT, and CEA Saclay, publishing in Science Advances, demonstrate a state of vibration that exists simultaneously at two different times, and provide evidence of this quantum superposition by measuring the strongest class of quantum correlations between light beams that interact with the vibration.
While many self-driving vehicles have achieved remarkable performance in simulations or initial trials, when tested on real streets, they are often unable to adapt their trajectories or movements based on those of other vehicles or agents in their surroundings. This is particularly true in situations that require a certain degree of negotiation, for instance, at intersections or on streets with multiple lanes.
Researchers at Stanford University recently created LUCIDGames, a computational technique that can predict and plan adaptive trajectories for autonomous vehicles. This technique, presented in a paper pre-published on arXiv, integrates an algorithm based on game theory and an estimation method.
“Following advancements in self-driving technology that took place over the past few years, we have observed that some driving maneuvers, such as turning left at an unprotected intersection, changing lanes or merging onto a crowded highway, can still be challenging for self-driving cars, while humans can execute them quite easily,” Simon Le Cleac’h, one of the researchers who carried out the study, told TechXplore. “We believe that these interactions involve a significant part of negotiation between the self-driving vehicle and the cars in its surroundings.”