Chip Industry Week in Review


Major Deals: Taiwan-based UMC is exploring possible collaboration with Polar Semiconductor for high-volume production of 8-inch wafers at Polar’s expanded Minnesota fab, a move that could provide domestic manufacturing capacity for automotive, data center, consumer, aerospace, and defense customers. Marvell will acquire Celestial AI for $3.25B, adding photonic fabric technology for o... » read more

Chip Industry Week In Review


China's Hefei Lumiverse Technology reportedly has developed a desktop-sized High Harmonic Generation light source that generates wavelengths as small as 1nm. One customer already has used it to produce 14nm chips, which was the original target node for EUV, according to one report. As a point of comparison, TSMC and Samsung didn't start using EUV until the 7nm node, relying instead on immersion... » read more

Chip Industry Week in Review


SK hynix is ramping HBM manufacturing capacity to meet explosive demand for AI data centers. The company will launch 16-stack HBM4 next year, and up to 12-stack HBM4E. HBM5 and HBM5E will be introduced between 2029 and 2031, reports Business Korea. China will not have access to NVIDIA’s most advanced chips, President Trump told 60 Minutes. The Dutch economy minister said Nexperia's chip... » read more

Chip Industry Week In Review


The EU’s tariffs on semiconductors will not exceed 15%, according to Trump’s latest trade deal. In addition, the EU committed to purchasing at least $40 billion worth of U.S. AI chips as well as other investments. [FAQ is here.] Lifelines for Intel: Intel inked a deal to sell the U.S. government a 10% non-voting equity stake in its business, worth $8.9 billion. The stake will be fun... » read more

Weird Incidents Reveal L5 Challenges


A series of surprising, counterintuitive, and sometimes bizarre incidents reveal the challenges of achieving full Level 5 autonomy in self-driving vehicles, which are an increasingly common site in major cities. While it’s easy to dismiss such anecdotes as humorous glitches compared with the sobering accounts of autonomous tech-related injuries and fatalities, industry executives say these oc... » read more

Week In Review: Manufacturing, Test


Onshoring and the supply chain Efforts to patch up supply chain weaknesses by moving more manufacturing onshore in the United States and Europe are generating a lot of buzz. Morris Chang, TMSC's founder, described those moves as "a very expensive exercise in futility," during an interview with the Brookings Institution and Center for Strategic and International Studies, adding that it is like... » read more

Week in Review – IoT, Security, Autos


Products/Services Rambus entered an exclusive agreement to acquire the Silicon IP, Secure Protocols, and Provisioning business from Verimatrix, formerly known as Inside Secure. Financial terms were not revealed. The transaction is expected to close this year. Rambus will use the Verimatrix offerings in such demanding applications as artificial intelligence, automotive, the Internet of Things, ... » read more

System Bits: Dec. 11


Calculating the costs of autonomous vehicles The development of autonomous vehicle technology commands a lot of media coverage. Little reporting has been devoted to the costs of operating AVs, a subject that developers don’t discuss in general. The Houston-Galveston Area Council’s website recently divulged contract figures with two startups, Drive.ai and EasyMile. For Silicon Valley-bas... » read more

Regulations Trail Autonomous Vehicles


Fragmented regulations and unrealistic expectations may be the biggest hurdles for chipmakers selling into the market for self-driving cars during the next few years. Carmakers and the semiconductor industry have made tremendous progress building real-time vision systems and artificial intelligence into relatively traditional automobiles during the past decade or so. But federal and state re... » read more

Security Holes In Machine Learning And AI


Machine learning and AI developers are starting to examine the integrity of training data, which in some cases will be used to train millions or even billions of devices. But this is the beginning of what will become a mammoth effort, because today no one is quite sure how that training data can be corrupted, or what to do about it if it is corrupted. Machine learning, deep learning and arti... » read more