South Korean President Lee Jae Myung delivers a speech on June 25. SEOUL - South Korea is set to unveil three "mega-projects" to fuel its next growth phase, including a new semiconductor hub in the southwest that local media say could attract investments by Samsung and SK spanning hundreds of billions of dollars over several years. The announcement would mark President Lee Jae Myung's boldest push yet to align South Korea's AI and chip ambitions with his pledge to narrow regional disparities and revive economies beyond the Seoul metropolitan area. Lee will preside over the event, framed as a national "great leap" due to be unveiled around 2 p.m., his office said, with ministries covering industry, science, climate and transport set to outline policy support. Samsung Electronics and SK are expected to present investment plans, and their chairmen, Jay Y. Lee and Chey Tae-won, are among business leaders tipped to attend by local media. Representatives of other firms including LG Electronics, HD Hyundai Robotics, Korea Electric Power Corp. and Korea Water Resources Corp. are also attending, Lee's office said.

Samsung's Excellent OLED Monitors Are Up to 36 Percent Off for Prime Day Samsung makes some of the very best OLED gaming monitors, and they've never been this affordable. Samsung makes some of the very best OLED monitors out there, and some of its top gaming monitors are getting some solid discounts for Prime Day. The strongest deal on offer is on the Odyssey G6 (G61SH). This 27-inch monitor is one of the company's latest displays, offering a 240-Hz refresh rate at a 2560 x 1440 resolution. It's been sold below its retail price off and on over the past few months, but this is the lowest it's ever dropped to.

Sony is halting sales of its Aibo robotic dog in Japan, ending an era for the interactive pet that became an instant hit and developed its own personality. Sony is halting sales of its Aibo robotic puppy in Japan, the company has said, eight years after the latest model of its interactive android pet became an instant hit. The Thursday announcement marks the end of an era for loyal fans of the high-tech toy, which develops its own personality and can perform tricks like waving and mimicking its owner. It was also a big comeback for Sony's robot dog. The first iteration of Aibo came out in 1999, followed by numerous models over the years -- from angular metallic-silver bots to more cuddly round-faced versions -- with more than 150,000 units sold. But by 2006, Sony, facing a tough business environment, pulled the plug on Aibo, seen as something of a frivolous luxury.

IBM hails new'block of flats' design breakthrough for ultra tiny chips Image caption, IBM's new sub-1 nm chip crams almost 100 billion transistors onto a surface the size of a fingernail IBM has unveiled a new chip design which it says could enable manufacturers to cram 100 billion transistors on a silicon chip the size of a fingernail. The current industry-standard size for chips, measured in a the unit of nanometres - a billionth of a metre and the size of a few atoms - is around two nanometres (nm). But IBM claims its new chip tech is the equivalent of around 0.7nm, which may make it the world's first known chip technology below 1nm. However, it will be several years before the chip tech could be ready to go into production. The firm claims in tests, its prototype performed 50% better than its own 2nm chip and was 70% more energy efficient.

Modular, one of the most promising chip software startups of the AI era, heads for a multibillion-dollar exit. Qualcomm will acquire the Silicon Valley chip startup Modular for nearly $4 billion. The companies announced the acquisition on Wednesday; Qualcomm said it expects to issue up to 19.2 million shares of common stock in the deal, which works out to just under $4 billion based on the company's last closing share price. The deal, which includes $300 million for Modular employees, comes nine months after the chip startup raised $250 million at a $1.6 billion valuation . It's expected to close in the second half of this year.

The AI era needs ever faster chips. ASML has a monopoly on the expensive contraptions needed to pattern them. Jos Benschop is climbing a ladder to get to the top of his newest machine. The contraption is the size of a double-decker bus--more than 150 tons of gleaming precision-milled aluminum covered in thousands of snaking tubes, colored cables, and pressurized tanks. From the ground, it looks like a futuristic V8 engine. When I reach the top with Benschop we're looking down from about 15 feet in the air, with bunny-suited technicians scurrying around below. It's more than 200 cubic meters of tech--"mechatronic devices that hold a few mirrors in a position with atomic precision," he says, gesturing at the gargantuan apparatus. Benschop, a tall and grizzled 66-year-old, has spent over a decade working with his engineers to design this thing, but even so, he'll sometimes look at it and go: Benschop is the executive vice president of technology for ASML, a Dutch company that is the linchpin of the microchip industry. If you want to make powerful chips to power phones or AI, a lithography machine like the one we're standing on is what you need to create increasingly tiny circuitry. Lithography is the art and science of shining light on a silicon wafer to pattern out the transistors, wiring, and other components of the microchips that will be cut from it. The chipmaking field is essentially controlled by only two big players: ASML, which creates the lithography machines, and TSMC, the chipmaking giant. Nine years ago, ASML began selling machines that use a daring new way of patterning chip features.

Rectilinear Steiner Minimum Tree (RSMT) is widely used in Very Large Scale Integration (VLSI) and aims at connecting a set of pins using rectilinear edges while minimizing wirelength. Recently, learning-based methods have been explored to tackle this problem effectively. However, existing methods either suffer from excessive exploration of the search space or rely on heuristic combinations that compromise effectiveness and efficiency, and this limitation becomes notably exacerbated when extended to the obstacle-avoiding RSMT (OARSMT). To address this, we propose OAREST, a reinforcement learning-based framework for constructing an Obstacle-Avoiding Rectilinear Edge Sequence (RES) Tree. We theoretically establish the optimality of RES in obstacle-avoiding scenarios, which forms the foundation of our approach. Leveraging this theoretical insight, we introduce a dynamic masking strategy that supports parallel training across varying numbers of pins and extends to obstacles during inference. Empirical evaluations on both synthetic and real-world benchmarks show superior effectiveness and efficiency for RSMT and OARSMT problems, particularly in handling obstacles without training on them.

Lithography orchestrates a symphony of light, mask and photochemicals to transfer the integrated circuit patterns onto the wafer. Lithography simulation serves as the critical nexus between circuit design and manufacturing, where its speed and accuracy fundamentally govern the optimization quality of downstream resolution enhancement techniques (RETs). While machine learning promises to circumvent computational limitations of lithography process through data-driven or physics-informed approximations of computational lithography, existing simulators suffer from inadequate lithographic awareness due to insufficient training data capturing essential process variations and mask correction rules.

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As a bio-inspired vision sensor, spike camera records light intensity by accumulating photons and firing a spike once a preset threshold is reached. For high-light regions, the accumulated photons may reach the threshold multiple times within a readout interval, while only one spike can be stored and read out, resulting in incorrect intensity representation and a limited dynamic range. Multi-level (ML) spike camera enhances the dynamic range by introducing a spike-firing counter (SFC) to count spikes within each readout interval for each pixel, and uses different spike symbols to represent the arrival of different amounts of photons. However, when the light intensity becomes even higher, each pixel requires an SFC with a higher bit depth, causing great cost to the manufacturing process. To address these issues, we propose time-encoding (TE) spike camera, which transforms the counting of spikes to recording of the time at which a specific number of spikes (i.e., an overflow) is reached.