From data to domain expertise: How AI, accelerated computing and digital twins are reshaping semiconductor manufacturing
The dawn of industrial AI in semiconductor manufacturing
A transformative moment is unfolding in the semiconductor manufacturing industry—a shift driven by artificial intelligence (AI), digital twins and GPU-accelerated computing. At the 2026 NVIDIA GTC, Danping Peng, vice president of research and development for the Calibre semiconductor manufacturing group at Siemens EDA, shared his insights into how these advancements are setting the foundation for a smarter, more agile industrial future.
As Peng explained, “We’re entering an era where AI is not just assisting our experts—it’s empowering every engineer to harness data, collaborate seamlessly and make better decisions instantly.”
Building value from data: The foundation for smarter manufacturing
Every device, vehicle and industrial process today generates massive streams of data. Data in the information age serves as the “oil”—a core asset packed with potential. But extracting actionable information from this abundance requires algorithms that are both powerful and purpose-built. Peng said that turning data into value starts with domain context. In semiconductor manufacturing, it’s essential that our models understand what matters to engineers and operators—accuracy, speed and safety.
Large language models represent a leap in AI versatility, but as Peng explained, “breadth comes with weakness.” These models are often generalists (“Jack of every trade, master of none”). For semiconductor professionals striving for precision and efficiency, the next step is clear: agentic AI.
Agentic AI: Turning domain knowledge into automation
Agentic AI brings practical, domain-specific intelligence to the world of semiconductor manufacturing. Rather than relying on AI models trained only on the world’s data, agentic AI transforms the know-how of expert engineers into digital agents. These agents use natural language, orchestrate complex workflows and deliver outcomes—often making specialized tasks accessible to a broader audience, including non-technical stakeholders.
As Peng summarized, “Agentic AI allows us to capture the wisdom of our best engineers and scale it across the organization. Now, teams can automate multi-step processes that used to require years of specialized experience.”
Practically, this means that planning, acting and delivering results no longer require deep programming knowledge. Teams can activate high-value automation, accelerating productivity and focusing human expertise where it matters most. For investors and stakeholders, the result is improved ROI driven by faster development and smarter decision-making.
Read more how Calibre manufacturing tools use AI/ML.
The Siemens EDA and NVIDIA partnership
Earlier this year, Siemens EDA announced an expanded partnership with NVIDIA, targeting four core areas:
- AI-driven simulation
- Supply chain management
- Manufacturing optimization
- Electronic design automation (EDA)
Siemens EDA sits at the nexus of manufacturing, engineering and digital transformation. Through our Xcelerator platform—a portfolio of software, hardware and services built to accelerate industrial innovation—the company supports global manufacturers in designing products, managing supply chains and optimizing production.
Peng emphasized the significance of this partnership: “Our collaboration with NVIDIA means simulation and automation can happen faster than ever before, with true-to-life context and physics. We’re leveraging industry-leading GPUs to drive the next decade of productivity for chip and system makers.”
By leveraging NVIDIA’s Omniverse, enabled Xcelerator with real-time collaboration among distributed engineering teams in photorealistic digital environments. The result: digital twins of manufacturing processes that provide instant feedback on the impact of process changes—a decisive advantage in time-to-value and operational agility.
Digital twins at scale: Simulating the factory of the future
The concept of the digital twin—a comprehensive, dynamic virtual representation of assets, products and processes—stands at the heart of digital transformation. With Siemens Xcelerator and Omniverse integration, digital twins now scale from singular processes to entire factory floors, vehicles and infrastructure ecosystems.
Peng explained, “The digital twin transforms how we design, build, and run manufacturing environments. With real-time simulation, engineering becomes more predictive and less reactive.”
These high-fidelity simulations allow manufacturers to test scenarios, analyze outcomes and validate designs before any physical investment. Feedback loops between the virtual and physical worlds become nearly instantaneous. Operational risk drops, while opportunities for optimization skyrocket—a fundamental enabler for next-generation, resilient supply chains.
Physics AI: Embedding the laws of the physical world into machine learning
Traditional AI models in manufacturing rely heavily on large volumes of historical and simulated data. However, collecting and annotating such data remains expensive and time-consuming, especially as semiconductor geometries shrink and manufacturing complexity increases.
Peng highlighted the opportunity: “Physics-based foundation models help us bridge the gap between what our engineers know and what our data can tell us. By embedding the laws of physics, we get the best of both worlds: trustworthy AI and faster results.”
Physics AI offers a way forward. By embedding physics-based constraints directly into AI models, Siemens EDA builds solutions that are more stable, predictive and faster to train. This approach narrows the gap between simulated results and real-world behavior, pushing beyond the limits of purely data-driven methods.
As sensor data, process telemetry and operational feedback are fused with computational models, manufacturers get systems capable of near real-time self-correction and optimization. The promise: robust, lower-cost manufacturing and rapid innovation cycles—even as traditional big data approaches hit scalability barriers.
Accelerated computing: The GPU-powered future of EDA and production
The complexity of semiconductor manufacturing and design today is staggering. Modern GPUs, for example, vastly outstrip their predecessors in raw parallelism and system integration, demanding new approaches to design, verification and yield management.
“GPUs have changed the game,” noted Peng. “They enable us to simulate, validate, and orchestrate designs on a scale we could only dream of a few years ago.”
GPU acceleration is now foundational in many Siemens EDA tools. It powers not only the visualization and manipulation of digital twins but also the solution of intricate physical equations at industrial scale. This shift enables orders-of-magnitude reduction in simulation and verification times, unlocking previously impossible optimization routines and design space exploration.
NVIDIA’s CUDA platform further extends these gains, empowering AI models that proactively monitor and correct manufacturing workflows in real time. For investors and engineering leaders, the message is clear: the tools shaping the next decade of silicon are being born on the platforms of today.
Embedded intelligence: Bringing generative and agentic AI to every workflow
In semiconductor manufacturing and EDA, automation is only as powerful as its ability to reason, adapt and act. The Siemens EDA Fuse EDA AI system is engineered to embed generative and agentic AI directly into every major workflow—from initial circuit design through manufacturing of integrated circuits and printed circuit board (PCB) systems.
“Our vision,” Peng said, “is to put reasoning AI right inside the loop for every engineer, designer, and operator. By blending agentic and generative models, we make innovation accessible—moving from complicated software to collaborative intelligence.”
This cohesive AI strategy ensures that knowledge is shared, processes are streamlined and complexity does not stand in the way of innovation. With the expanded NVIDIA partnership announced at GTC, Siemens EDA now offers AI-powered reasoning and autonomous process management in the IC and PCB product lines—changing the nature of engineering collaboration.
The implications reach beyond speed. AI agents can now automate multi-step workflows, resolve bottlenecks and perform tradeoff analyses that once demanded expert intervention, freeing designers to focus on creativity and differentiation.
The future: Smarter factories, faster operations and greater value
Semiconductor professionals, investors and OEMs are witnessing an unprecedented transformation. With its deep domain expertise and ecosystem partnerships, Siemens Digital Industries Software is positioned to enable a future where industrial teams collaborate with AI agents, digital twins bridge the virtual and real and the boundaries of what’s possible are continually redefined.
By harnessing agentic AI, physics-based modeling and accelerated computing, organizations can expect:
- Dramatically improved time-to-market and time-to-productivity
- Resilient, flexible and efficient supply chains
- Lower operational costs and fewer process failures
- Higher-quality, more affordable products for every market segment
Peng summarized, “This is an inflection point. The integration of AI, digital twins, and GPU acceleration will unlock the next wave of productivity, resilience, and creativity for the world’s most demanding industries.”
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