Chips & Supply · United States

AI Data Centers Drive Demand for SiC, GaN Power Semiconductors

Credited to TheElec · thelec.net · Just now

Power consumption is rising with the spread of artificial intelligence (AI) data centers, highlighting the importance of compound power semiconductors such as silicon carbide (SiC) and gallium nitride (GaN). Data centers are adopting high-voltage power networks to deliver more electricity efficiently. Conventional silicon (Si) power semiconductors have limitations in reducing power losses.

Bae Sung-beom, a senior principal researcher at the Electronics and Telecommunications Research Institute (ETRI), said Oct. 7 at the Korea Power Device Industry Association's Power Semiconductor Industry-Academia-Research K-Forum at Hotel Nongshim in Busan that the growth of the AI industry is causing power shortages worldwide. “Because it is difficult to expand power infrastructure at the pace of actual demand growth, it is becoming increasingly important to use limited energy efficiently,” Bae said.

Data centers operate large-scale computing resources such as graphics processing units (GPUs) around the clock. As AI models grow larger, the amount of GPUs and storage capacity required by data centers increases, driving up power consumption. How efficiently electricity supplied from external power grids can be delivered to servers with minimal losses is also becoming increasingly important.

Improving power efficiency requires reducing losses that occur when electricity from outside is converted to voltages that servers or GPUs can handle. NVIDIA's 800-volt direct-current (DC) data center power architecture has emerged against this backdrop. The 800V DC system distributes electricity inside data centers at a higher 800-volt DC voltage than conventional systems. It simplifies the existing power-supply architecture and transmits electricity at higher voltage, reducing losses during power conversion and wiring.

As power-supply voltages rise, the role of power semiconductors becomes more important. Improving power efficiency in high-voltage and high-power environments has limitations because designing devices to withstand higher voltages makes it more difficult to reduce power losses and heat generation.

Compound power semiconductors such as SiC and GaN are being considered as alternatives. They can operate at higher voltages and temperatures than Si and reduce losses during power conversion. Their ability to switch at high speeds also helps improve the efficiency of power-conversion equipment and enables smaller components.

Applications vary depending on voltage levels and power-conversion stages. SiC is suited to high-voltage, high-power environments. It can be used in equipment that converts electricity supplied from external power grids at thousands to tens of thousands of volts into power that can be used inside data centers. GaN offers fast switching performance at relatively lower voltage levels. It can help reduce power losses when voltage is stepped down inside servers to levels required by GPUs and other computing devices.

Reliability issues need to be addressed for commercialization. Compound semiconductors are manufactured by combining different materials to form devices. Differences in the coefficients of thermal expansion between materials can cause wafers to warp or crack. The thermal conductivity of substrates also affects device output and efficiency. The industry is researching ways to address these issues using new substrate materials and vertical device structures.

CapitalUnited States

Sigurd raises prices, signs long AI capacity contracts, eyes higher 2026 capex

Credited to DigiTimes$ · digitimes.com11h ago

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Taiwanese OSAT provider Sigurd Microelectronics and its subsidiary Winstek Semiconductor held a joint investor briefing on October 6. President Charles Yeh reported that surging demand across AI CPUs, GPUs, ASICs, silicon photonics, and memory is severely tightening testing…

View the article · $DigiTimes

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Chips & Supply · United States

Vinci Raises $250M at $1.5B Valuation for Continuous Physics Reasoning

Credited to Converge Digest · convergedigest.com · 1d ago

Photograph · Converge Digest

Vinci has raised $250 million in Series B funding at a $1.5 billion valuation to expand its AI-native computational platform for hardware engineering. The round was co-led by Advent, Temasek and Xora Innovation, with participation from AMD Ventures, Madrona, Eclipse, Khosla Ventures and other investors. The financing follows Vinci’s emergence from stealth in December 2025 with $46 million in previously announced funding, bringing disclosed funding to approximately $296 million.

The Palo Alto company is developing what it calls Continuous Physics Reasoning: an engineering architecture intended to move physics simulation from discrete checkpoints into the active design process. Vinci says its platform combines automated design preparation, agentic orchestration, a Foundation Model for Physics and GPU-native physics kernels. The company says it can analyze manufacturing-scale designs ranging from hundreds of millions to more than 15 billion degrees of freedom in minutes rather than hours or days, without customer-specific model training or fine-tuning.

PLATFORM — Continuous Physics Reasoning • Foundation Model for Physics • GPU-native physics kernels • Agentic orchestration

CURRENT PHYSICS — Thermal • Thermo-mechanical • Convective fluid behavior

EXPANSION — Memory • Advanced computing • Vehicles • Aircraft • Satellites • Additional physics domains

The new capital will fund expansion across both physics domains and engineering workflows. Vinci plans to move beyond its semiconductor starting point toward memory systems, advanced computing, vehicles, aircraft and satellites, while adding additional physical phenomena. The company’s longer-term ambition is to move from predicting what will happen toward helping engineers determine what should change and ultimately generate increasingly complex physical designs from human intent.

“Designing advanced systems requires engineers to understand how thermal, physical and electrical behavior interact across the chip, package and board,” said Brian Amick, senior vice president of technology and engineering at AMD. He said faster simulation can let engineering teams evaluate tradeoffs and architectural decisions earlier in the design process.

Kabaria explains how Vinci’s continuous physics reasoning platform makes thermal analysis accessible across the entire hardware engineering process—from semiconductors and high-bandwidth memory to complete data center racks and satellites. The company’s AI-powered solution delivers accurate, deterministic results while maintaining data privacy by deploying behind customer firewalls. Unlike traditional approaches, Vinci’s pre-trained foundation model requires no fine-tuning and provides orders-of-magnitude faster analysis without sacrificing accuracy.

The conversation explores Vinci’s current market validation with leading hardware companies across the US, Europe, and Asia, and reveals the company’s ambitious roadmap to expand beyond thermal physics into fluid dynamics, electromagnetics, and other critical physical phenomena that govern hardware performance.

– How continuous physics reasoning transforms hardware design workflows – Why AI-powered thermal analysis is critical for modern AI infrastructure – How Vinci’s platform maintains accuracy while delivering enterprise-grade security – The company’s approach to pre-trained physics models that work out of the box – Vinci’s expansion plans into additional physics modalities beyond thermal analysis

📚 CHAPTERS: 0:00:00 – Introduction to Vinci and Series B Funding Announcement 0:00:36 – Defining Continuous Physics Reasoning in Hardware Engineering 0:02:30 – AI-Powered Product Architecture and Customer Requirements 0:04:37 – Thermal Analysis Applications in AI Infrastructure 0:06:09 – Current Market Position and Customer Validation 0:06:58 – Future Expansion Beyond Thermal Physics

Have a tech update that you want to brief us on? Contact info@nextgeninfra.io!

Vinci is entering a strategically important part of engineering software: applying AI not simply to automate EDA tasks, but to make computational physics available much earlier and more continuously in hardware design. This is particularly relevant for AI accelerators and advanced packages, where thermal behavior, mechanical stress, cooling, power delivery, signal integrity and packaging increasingly have to be evaluated as interacting system-level constraints.

Vinci’s differentiation centers on its assertion that its Foundation Model for Physics can operate zero-shot on previously unseen designs while retaining deterministic, solver-level accuracy. Those performance and accuracy descriptions remain company claims rather than independently established benchmarks across the full range of engineering workloads.

The Series B gives Vinci substantially more resources to test whether this architecture can expand from semiconductor thermal and thermo-mechanical analysis into a broader engineering platform. The larger strategic question is whether physics reasoning can become an interactive layer inside engineering workflows rather than a specialist-driven simulation step performed periodically during design.

Vinci AI-Native Computational Engineering Platform

Core Technology — Continuous Physics Reasoning • Foundation Model for Physics • GPU-native physics kernels

APRIL 28, 2026 · AI INFRASTRUCTURE Vinci: Building Continuous Physics Intelligence for Better AI Infrastructure CEO Hardik Kabaria discusses physics challenges spanning memory, GPUs, servers and data centers.

SEPTEMBER 29, 2026 · SEMICONDUCTORS Synopsys Unveils AgentEngineer and Autopilot Platform for Autonomous Engineering

JULY 17, 2026 · SEMICONDUCTORS Rapidus, Cadence Target 2X Faster SoC Design with Agentic AI