Chips & Supply · United States

NEXTIN Wins 25.44 Billion Won Wafer Inspection Equipment Order From SK hynix

Credited to TheElec · thelec.net · 14h ago

Semiconductor inspection and metrology equipment maker NEXTIN has won an order worth 25.44 billion won for equipment.

On Oct. 6, NEXTIN announced that it had signed a contract with SK hynix to supply a Wafer Inspection System. The contract is worth about 25.44 billion won, equivalent to 38.02% of NEXTIN's sales of 66.9 billion won last year.

The equipment to be supplied is understood to be from the AEGIS series. AEGIS is a front-end semiconductor inspection system based on 2D imaging technology that inspects the wafer during the process in which fine circuits are formed.

The system shines light onto the wafer surface and captures circuit images using multiple complementary metal-oxide-semiconductor (CMOS) sensors. It detects nanoscale defects and particles in fine circuits by comparing the images with normal pattern images.

The equipment incorporates both bright-field (BF), which uses reflected light, and dark-field (DF), which uses scattered light, optical systems. Inspection modes can be selected according to process characteristics. The system offers greater price competitiveness and process efficiency than competing equipment, which separates BF and DF systems into different machines.

The equipment will be supplied in South Korea. It is estimated to be intended for Y1, the first fab at SK hynix's new semiconductor industrial complex in Yongin.

The contract ends on Nov. 25 next year. NEXTIN will complete equipment manufacturing, delivery, installation and setup before the contract expiration date.

There is no separate advance payment. The company did not disclose the payment terms for the equipment. Semiconductor manufacturers typically make payments after equipment setup is completed.

Power & SitesUnited States

LG Electronics Signs 5GW Cooling Deal for North American AI Data Centers

Credited to TheElec · thelec.net15h ago

Useful

LG Electronics plans to pursue a cooling-solutions supply business totaling 5 gigawatts (GW) in North America.

LG Electronics said Oct. 6 that it had signed a definitive long-term agreement to supply chillers for AI data centers (AIDC) with infrastructure platform company Air Control Concepts (AIR).

Under the agreement, LG Electronics will gradually supply cooling solutions, including chillers used to manage heat in data centers, to facilities in the US and Canada. The company did not disclose specific delivery schedules, customers or contract values.

Air Control Concepts is an infrastructure platform company that oversees the full lifecycle of AIDC projects in North America, including design, construction and public procurement. It carries out large-scale projects and counts major AIDC companies around the world among its key customers. The company specializes in cooling and HVAC infrastructure solutions.

LG Electronics began developing its chiller business in earnest in 2011. It started supplying cooling solutions this year for a major North American AIDC project secured last year. The company also supplies cooling solutions to markets in the Global South, including the Middle East and Indonesia.

According to market research firm Omdia, global power capacity required by AIDCs is projected to rise by 108 GW, from 226 GW in 2026 to 334 GW in 2027.

Original · TheElec

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Power & Sites · United States

Axelera AI: Data Center Inference Performance in the Power Envelope of Embedded Systems

Credited to EE Times · eetimes.com · 19h ago

Photograph · EE Times

Partner Content

Physical AI is hitting three walls: power, economics, and fragmentation. Fabrizio Del Maffeo explains how to break them.

Recorded at the AI Infra Summit 2026 in Santa Clara, California, Axelera AI co-founder and CEO Fabrizio Del Maffeo sits down with EE Times Sally Ward-Foxton on why intelligence was never the bottleneck to AI adoption. Infrastructure is.

The three walls to scaling physical AI

Del Maffeo’s keynote framed three barriers standing between today’s pilots and real deployment.

Power and physics. The data center was built for training: persistent, high-wattage, liquid-cooled, and tolerant of moving data long distances. Physical AI is the inverse. Inference happens where data is created—in factories, vehicles, hospitals, and city infrastructure—inside power envelopes measured in tens of watts. Shrinking a training architecture does not fix that, because the dominant cost is not computation; it is data movement. Axelera’s Digital In-Memory Compute performs matrix-vector multiplication, roughly 80% of the inference workload, inside the memory cell itself. Less movement, less energy, more work per watt.

Economics and control. Cloud inference made sense for experimentation. For production workloads running continuously, the bill never stops growing, and in regulated industries, the data cannot leave the building in the first place. Del Maffeo argues the decision has shifted from a cost question to a control question: your models, your data, your cost structure.

Fragmentation. Every new accelerator brings its own toolchain, conversion step, and lock-in. Developers pay that tax, and it is why projects stall between demo and deployment.

Start at the edge, then scale up

Most semiconductor companies design for the cloud and strip the architecture down for smaller devices. Axelera did the opposite: solve the hardest, most power-constrained environments first, then carry that efficiency up. Europa, launched at the show, is what that looks like at enterprise scale.

Europa is a purpose-built inference accelerator, delivering 629 TOPS at INT8 with eight second-generation AI cores, 16 on-chip RISC-V vector processors for pre- and post-processing, LPDDR5 at 200 GB/s, hardware HEVC/H.265 decode, and a Kudelski KSE3 secure enclave, at 35 watts typical during LLM inference. It ships as the Axelera Edge 232p, a half-height, half-length PCIe card, and the Axelera Server 250p, a full-height, full-length card with four AI processing units. Standard slot, existing server, no new rack, no new cooling.

Software is the missing link

Silicon is the easy half. Del Maffeo makes the case that hardware and software have to be co-designed, and that manual optimization is where developer velocity dies. The Voyager SDK takes PyTorch models directly with no intermediate conversion, covering CNNs, transformer vision models, VLMs and LLMs, with quantization and graph optimization handled by the compiler. Voyager Wingman, the new agentic developer assistant, turns plain language into a working pipeline, runs it on real hardware, and iterates until it performs. Built on RISC-V, open by design, no proprietary lock-in.

Ecosystem validation

A chip alone deploys nothing. Del Maffeo closes on the partner ecosystem, from defense and agricultural robotics to retail analytics and rail, and how system integrators, model builders, and hardware vendors working against one toolchain are collapsing the traditional 12- to 18-month deployment cycle.

https://axelera.ai/ai-accelerators/aipu/europa

https://axelera.ai/news/axelera-ai-launches-europa-delivers-physical-and-enterprise-ai-through-growing-partner-ecosystem-including-dell-and-supermicro

https://axelera.ai/ai-software/voyager-sdk#wingman