Taiwan has officially banned the export of chips and chiplets to China’s Huawei and SMIC, joining the US in tightening restrictions on advanced semiconductor transfers.
The decision follows reports that TSMC, the world’s largest contract chipmaker, was unknowingly misled into supplying chiplets used in Huawei’s Ascend 910B AI accelerator. The US Commerce Department had reportedly considered a fine of over $1 billion against TSMC for that incident.
Taiwan’s new rules aim to prevent further breaches by requiring export permits for any transactions with Huawei or SMIC.
The distinction between chips and chiplets is key to the case. Traditional chips are built as single-die monoliths using the same process node, while chiplets are modular and can combine various specialised components, such as CPU or AI cores.
Huawei allegedly used shell companies to acquire chiplets from TSMC, bypassing existing US restrictions. If TSMC had known the true customer, it likely would have withheld the order. Taiwan’s new export controls are designed to ensure stricter oversight of future transactions and prevent repeat deceptions.
The broader geopolitical stakes are clear. Taiwan views the transfer of advanced chips to China as a national security threat, given Beijing’s ambitions to reunify with Taiwan and the potential militarisation of high-end semiconductors.
With Huawei claiming its processors are nearly on par with Western chips—though analysts argue they lag two to three generations behind—the export ban could further isolate China’s chipmakers.
Speculation persists that Taiwan’s move was partly influenced by negotiations with the US to avoid the proposed fine on TSMC, bringing both countries into closer alignment on chip sanctions.
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Researchers at the Quantum Systems Accelerator have announced significant progress in building scalable, stable quantum computers focusing on trapped-ion technology.
Their work marks a series of engineering milestones pushing quantum computing toward practical use.
A new ion trap chip can store up to 200 ions and significantly reduces power loss by redesigning its internal layout.
Developed and tested with collaborators at Duke and Cornell in the US, this design allows for the future creation of far larger qubit systems without overheating or energy waste.
At the University of Maryland, a team achieved parallel quantum gate operations using different spatial directions, overcoming prior interference issues.
However, this innovation boosts processing speed and accuracy, offering more efficient handling of time-sensitive quantum tasks.
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What began as a company powering 3D games in the 1990s has evolved into the backbone of the global AI revolution. Nvidia, once best known for its Riva TNT2 chips in consumer graphics cards like the Elsa Erazor III, now sits at the centre of scientific computing, defence, and national-scale innovation.
While gaming remains part of its identity—with record revenue of $3.8 billion in Q1 FY2026—it now accounts for less than 9% of Nvidia’s $44.1 billion total revenue. The company’s trajectory reflects its founder Jensen Huang’s ambition to lead beyond the gaming space, targeting AI, supercomputing, and global infrastructure.
Recent announcements reinforce this shift. Huang joined UK Prime Minister Sir Keir Starmer to open London Tech Week, affirming Nvidia’s commitment to launch an AI lab in the UK, as the government commits £1 billion to AI compute by 2030.
Nvidia also revealed its Rubin-Vera superchip will power Germany’s ‘Blue Lion’ supercomputer, and its Grace Hopper platform is at the heart of Jupiter—Europe’s first exascale AI system, located at the Jülich Supercomputing Centre.
Nvidia’s presence now spans continents and disciplines, from powering national research to driving breakthroughs in climate modelling, quantum computing, and structural biology.
‘AI will supercharge scientific discovery and industrial innovation,’ said Huang. And with systems like Jupiter poised to run a quintillion operations per second, the company’s growth story is far from over.
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IBM has set out a detailed roadmap to deliver a practical quantum computer by 2029, marking a major milestone in its long-term strategy.
The company plans to build its ‘Starling’ quantum system at a new data centre in Poughkeepsie, New York, targeting around 200 logical qubits—enough to begin outperforming classical computers in specific tasks instead of lagging due to error correction limitations.
Quantum computers rely on qubits to perform complex calculations, but high error rates have held back their potential. IBM shifted its approach in 2019, designing error-correction algorithms based on real, manufacturable chips instead of theoretical models.
The change, as the company says, will significantly reduce the qubits needed to fix errors.
With confidence in its new method, IBM will build a series of quantum systems until 2027, each advancing toward a larger, more capable machine.
Vice President Jay Gambetta stated the key scientific questions have already been resolved, meaning what remains is primarily an engineering challenge instead of a scientific one.
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Taiwan Semiconductor Manufacturing Co. (TSMC), the world’s leading contract chipmaker, reported a significant 39.6% year-over-year surge in May revenue, reaching NT$320.52 billion ($10.70 billion).
This robust growth is primarily attributed to sustained high demand for its AI chips. The company, a key supplier to tech giants like Apple and Nvidia, has seen its US-listed shares rise over 2% in premarket trading, extending their 5% gain so far this year.
Despite May’s revenue being down 8% from April’s figure, the chipmaker’s January-to-May revenue climbed nearly 43% compared to the same period last year, reaching NT$1.51 trillion.
This strong performance underpins TSMC’s ambitious expansion plans, including a previously announced intent to invest $100 billion in U.S.-based chip-manufacturing facilities.
TSMC CEO C.C. Wei reiterated the company’s full-year 2025 revenue projection in April, anticipating an increase of ‘close to mid-20s percent in US dollar terms.’
The continued strong demand for AI chips is expected to be a major driver in achieving these financial targets, solidifying TSMC’s critical role in the global technology landscape.
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Qualcomm has agreed to acquire London-listed semiconductor firm Alphawave for approximately $2.4 billion in cash, aiming to strengthen its position in AI and data centre technologies. Alphawave shares surged 23% in London trading following the announcement.
The deal, offering 183 pence per share, represents a 96% premium over Alphawave’s share price at the end of March. Regulatory and shareholder approvals are still required, with the transaction expected to close in early 2026.
Qualcomm is diversifying beyond smartphones as CEO Cristiano Amon targets growth sectors such as AI hardware. Alphawave, known for high-speed chip connectivity, has gained momentum, especially among US AI customers.
Alphawave’s board unanimously supports the offer, and shareholders representing half the company have already agreed to the deal. In addition to the cash option, Qualcomm is offering stock and security exchange alternatives.
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A small-scale quantum device developed by researchers at the University of Vienna has outperformed advanced classical machine learning algorithms—including some used in today’s leading AI systems—using just two photons and a glass chip.
The experiment suggests that useful quantum advantage could arrive far sooner than previously thought, not in massive future machines but in today’s modest photonic setups.
The team’s six-mode processor doesn’t rely on raw speed to beat traditional systems. Instead, it harnesses a uniquely quantum property: the way identical particles interfere. This interference naturally computes mathematical structures known as permanents, which are computationally expensive for classical systems.
By embedding these quantum calculations into a pattern-recognition task, the researchers consistently achieved higher classification accuracy across multiple datasets.
Crucially, the device operates with extreme energy efficiency, offering a promising route to sustainable AI. Co-author Iris Agresti highlighted the growing energy costs of modern machine learning and pointed to photonic quantum systems as a potential solution.
These early results could pave the way for new applications in areas where training data is limited and classical methods fall short—redefining the future of AI and quantum computing alike.
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SoftBank and Intel are collaborating on a new memory technology designed to halve power consumption compared to current high-bandwidth memory (HBM), which is critical for AI systems.
The project includes the University of Tokyo, research institute Riken, and Shinco Electric Industries, among others, and aims to address cost, efficiency, and supply issues in today’s HBM market.
The initiative will be led by a newly established SoftBank subsidiary, Cy Memory, which is tasked with IP management and developing prototype memory chips that reconfigure wiring structures in stacked DRAMs.
The goal is to mass-produce more energy-efficient and cost-effective memory than existing HBM, with applications in data centres running AI workloads.
SoftBank plans to invest 3 billion yen in Cy Memory, becoming its largest shareholder, and may seek government funding.
As AI data centre demand skyrockets, the new memory could offer Japan a strategic edge in semiconductor development—particularly as domestic DRAM production has largely disappeared. Japan has pledged over 10 trillion yen in semiconductor investments by 2030 to revive its chip industry.
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Broadcom reported strong second-quarter earnings and revenue driven by robust AI demand and solid networking performance.
Despite beating expectations and raising its outlook, the stock fell 3.47% in after-hours trading on Thursday — likely due to profit-taking instead of concern about fundamentals. Shares had previously rallied over 75% since April.
Revenue for the quarter ending May 5 reached US$15 billion, up 20% year-on-year. Adjusted earnings per share were US$1.58, exceeding estimates by two cents.
Net income more than doubled to US$4.97 billion. CEO Hock Tan attributed the strength to growing demand for AI infrastructure and contributions from VMware, which Broadcom acquired in late 2023.
Broadcom forecasted Q3 revenue of approximately US$15.8 billion, slightly above analyst expectations. AI-related revenue is set to increase to US$5.1 billion, up from US$4.4 billion in Q2, fuelled by custom AI accelerators and high-speed networking chips used in hyperscale data centres.
Tan said that the trend should continue through fiscal 2026.
Semiconductor solutions brought in US$8.4 billion in Q2, up 17% from last year, while software revenue rose 25% to US$6.6 billion, with VMware as a key contributor.
About 30% of Broadcom’s AI-related revenue now comes from its switching business, reflecting increasing demand for AI chip clusters. Despite the slight dip in share price, analysts continue to view Broadcom as a key player in AI infrastructure.
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The global AI chip design market is set for explosive growth, with its value projected to rise from USD 73.87 billion in 2024 to USD 468.9 billion by 2032.
This rapid expansion, driven by a 25.98% compound annual growth rate, reflects rising demand for AI in everyday devices, cloud computing, and industrial automation.
Surging adoption of AI-powered technologies across sectors—from smartphones and autonomous vehicles to manufacturing and healthcare—is fuelling the need for advanced, energy-efficient chips.
Companies are investing in smaller, faster processors and real-time edge computing solutions, while governments in countries like the US, China, and South Korea are backing local AI chip development through funding and research initiatives.
North America currently leads the market thanks to tech giants like NVIDIA, Google, and Intel, but Asia-Pacific is growing fastest, particularly as China and South Korea accelerate efforts toward self-reliance in AI hardware.
Industry leaders such as Arm, Intel, and Qualcomm are racing to optimise performance while managing power demands, placing AI chip design at the heart of the next digital transformation wave.
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