For Intel Foundry, the announcement sends two important messages. Firstly, Intel Foundry continues to conduct long-term research on technologies that will be needed years, if not decades, away, meaning that it will have solutions for the semiconductor industry in the 2030s or 2040s, and, therefore, is a reliable manufacturing partner. Secondly, Intel shows that even at the research stage, new transistor concepts must be developed with manufacturability in mind.
Follow Tom's Hardware on Google News , or add us as a preferred source , to get our latest news, analysis, & reviews in your feeds.
Anton Shilov Social Links Navigation Contributing Writer Anton Shilov is a contributing writer at Tom’s Hardware. Over the past couple of decades, he has covered everything from CPUs and GPUs to supercomputers and from modern process technologies and latest fab tools to high-tech industry trends.
Diogene7 I wish much, much more ressources would be allocated to ferroelectricity and spintronics architectures, e.g: Spintec FESO FESO (Ferroelectric–Spintronic) devices offer several fundamental advantages over conventional silicon CMOS transistors. Unlike CMOS, which is inherently volatile and requires continuous power to retain state, FESO devices are intrinsically non-volatile. Information is stored directly in the physical state of the device (ferroelectric polarization and magnetization), enabling instant-on operation and near-zero standby power. Moreover, FESO naturally combines memory and computation in the same device. In CMOS systems, logic and memory are physically separated, leading to the well-known von Neumann bottleneck and excessive data movement. FESO eliminates much of this overhead by enabling in-memory and stateful computation, dramatically improving energy efficiency. Another key advantage is that FESO supports both analog and digital operation. Synaptic weights for AI can be stored and updated incrementally in an analog manner using device physics, while the same technology can also operate digitally when required. This flexibility is extremely difficult to achieve with CMOS transistors alone. Finally, FESO enables continuous, local learning AI. Because weights are persistent and updated directly at the device level, learning does not require frequent access to external volatile memory or global weight refresh. This opens the door to always-on, adaptive, brain-like systems that are fundamentally out of reach for purely CMOS-based architectures. In short, while CMOS excels at fast, deterministic digital logic, FESO introduces persistence, statefulness, and co-located memory and computation—properties that are essential for the next generation of energy-efficient and continuously learning AI hardware. Reply
Key considerations
- Investor positioning can change fast
- Volatility remains possible near catalysts
- Macro rates and liquidity can dominate flows
Reference reading
- https://www.tomshardware.com/tech-industry/semiconductors/SPONSORED_LINK_URL
- https://www.tomshardware.com/tech-industry/semiconductors/intel-shows-300-mm-fab-compatible-integration-of-2d-transistor-contacts-and-gate-stacks#main
- https://www.tomshardware.com
- Bernie Sanders calls for halt on AI data center construction — wants to ensure that the technology benefits ‘all of us, not just the 1%’
- Huawei's AI chip capabilities still pale in comparison to American silicon — report from U.S. council details that despite fears, Nvidia continues to lead by a
- '$100 Steam Machine' uses a cut-down PS5 APU with Bazzite — DIY console offers 60 FPS at 1080p with 16GB of GDDR6
- Boy breaks 50 of his father's Samsung M.2 NVMe SSDs worth nearly $4,000 — 25,600 GB of storage ruined by ten-year-old oblivious to global NAND crisis
- As AI Grows More Complex, Model Builders Rely on NVIDIA
Informational only. No financial advice. Do your own research.