A 300 millimetre silicon wafer. 

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I wrote an article on LAM Research , LAM, this year about their recent competitions. I also wrote an article in 2023 that mentioned their AI methodology for semiconductor process design and improvement. LAM calls this approach, the Semiverse. This article discusses similar AI design work at Applied Materials, AMAT, as well as developments in semiconductor chip, system and manufacturing design from Synopsys and Seimens. These companies are creating tools to enable greater AI automation in electronics design and test.

A few weeks also I also visited AMAT and they also told me that they have AI tools that they have developed for their part of the semiconductor process design. The AMAT Actionable Insight Accelerator, Aix, approach enables the creation of digital twins of semiconductor manufacturing processes combined with data from real time sensing and metrology of the semiconductor manufacturing environment.

AMAT says that this approach enables engineers to see into semiconductor processes in real-time, take millions of measurements across wafers and individual chips, and optimize thousands of process variables to improve semiconductor performance, power, area, cost and time to market.

At the 2026 IEEE Design Automation Conference, DAC, Synopsys announced an effort to enable end-to-end semiconductor system automation. The company said that their fully autonomous long-running design verification agent can orchestrates the entire chip verification cycle delivering up to 50X faster time-to-validated RTL while achieving 20% additional coverage improvement. This agentic engineering design approach was developed in collaboration with NVIDIA. Elements in this combined EDA and CAE methodology are shown in the image below.

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This was enabled with NVIDIA Nemotron on NVIDIA's accelerated computing platform and secured by the NVIDIA OpenShell runtime. It was demonstrated at DAC for the first time,

This fully autonomous computer-aided engineering, CAE, workflow provides thermal management and electronic device cooling is capable of autonomously executing set-up, pre-processing, and post-processing in a fraction of the time required for manual approaches

Synopsys said that this promises to be a force multiplier for R&D teams beyond task agents, transforming time-consuming chip verification and thermal simulation into automated insight delivery, engineering productivity, and system performance improvement engines.

Siemens, another major player in the semiconductor device EDA industry, also made an announcement of AI in their tools at the 2026 IEEE DAC. Seimens said that they also had a strategic partnership with NVIDIA to deliver self-verifying agentic AI workflows to EDA, helping semiconductor and printed circuit board (PCB) engineering teams move from autonomous task orchestration toward more trusted, continuously validated engineering outcomes.

The new capabilities build on Siemens’ recently introduced Fuse EDA AI Agent system and add new NVIDIA AI technology to help long-running, domain scoped, AI agents reason, act and continuously validate decisions against deterministic, physics-based EDA engines.

The Seimens’ approach appears to have a broader reach. The company said that the Fuse EDA AI Agent system is now integrated into Siemens’ recently launched Intelligence Center X, supporting agent creation and orchestration in Siemens’ enterprise industrial AI environment. Intelligence Center X delivers AI-driven, coordinated processes across design, manufacturing and supply chain – enhancing the comprehensive Digital Twin to support smarter execution and more trusted outcomes.

While these tools are useful for experienced electronic and semiconductor process design engineers and they can accelerate time to market, they should be used with some degree of caution. Ensuring reliability will require external observability through sandboxing, along with other safeguards in case something does go wrong. In addition, basic knowledge about semiconductor design and semiconductor processing are necessary to most effectively use tools like these.

However, agentic AI is fundamentally changing how engineers approach process design, electronics design and verification and the ability to effectively use these tools will be a requirement to work in this industry.