Siemens Expands Agentic AI for 3D IC Design
The company integrates its EDA tools with NVIDIA computing to automate complex electrothermal analysis for semiconductors.
Updated on Oct. 26, 2026 in Semiconductors
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Siemens has expanded its agentic AI electronic design automation portfolio to support advanced 3D IC electrothermal codesign. This new workflow orchestrates seven specialized EDA tools to automate complex design evaluations.
Why it matters
Rising power density and heat concentration in 3D integrated circuits demand earlier analysis during the design cycle to ensure performance. Automating these evaluations helps engineers manage complex physical tradeoffs more efficiently.
The system automates nine stages across seven tools, enabling the sweeping of 200 to 500 design points per cycle with a total runtime of 8 to 10 hours. It utilizes NVIDIA Rubin GPU inference scale reaching 50 petaflops and 288 gigabytes of HBM4 memory per unit.
The players
Siemens
This global technology conglomerate provides advanced software solutions for industrial automation and integrated circuit design.
NVIDIA
This technology company designs graphics processing units and develops computing platforms utilized for artificial intelligence and accelerated processing.
The details
Engineers define design objectives, and an AI agent directs the automated workflow to test design alternatives against physics-based solvers. The system identifies root causes for issues related to die placement, materials, and power delivery while reducing setup time by 99 percent.
Timeline
Siemens introduced its initial agentic AI capabilities in June 2026.
The expanded agentic AI EDA portfolio was announced on October 26, 2026.
The Tech Race
This integration moves the semiconductor industry toward autonomous design loops, replacing legacy manual iterative processes. It positions Siemens and NVIDIA to capture market share by accelerating the development cycles for next-generation 3D integrated circuits.
Semiconductor designers will see significant workflow improvements through the reduction of manual setup and analysis time. By streamlining complex thermal testing, the technology facilitates faster release cycles for the high-performance chips used in modern consumer electronics.
The takeaway
Automating design exploration allows engineers to focus on higher-level innovation rather than repetitive configuration tasks. Companies implementing these AI agents can significantly improve their time-to-market by identifying thermal constraints much earlier in the cycle.
Further reading
For more on evolving design workflows, visit the Semiconductors section.
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Do you believe that using AI to automate complex engineering tasks improves the quality of technology?
