How AI in semiconductor manufacturing is transforming chip design

Insights

• Artificial intelligence (AI) in semiconductor design enhances innovation, improves efficiency, and accelerates development, addressing the complexity and challenges in the ecosystem.
• Design accounts for over 53% of the semiconductor industry's R&D expenses, highlighting its importance for the sector's growth and innovation.
• Generative AI-driven solutions, such as automated IP search assistants, can improve productivity, reduce time-to-market, and optimize design processes.
• AI-powered floor planning and error log analysis improve design efficiency by automating complex tasks and reducing manual effort.
• The use of AI-driven tools like design copilots helps design engineers overcome collaboration challenges, and streamlining communication and decision-making across teams.

The semiconductor industry is on track to become a trillion-dollar industry by 2030, fuelled by requirements for advanced chips in AI, 5G/6G communications, and data centers. While there is growth across the semiconductor value chain, a significant portion of value creation is driven by semiconductor design companies. Among the top 20 global semiconductor companies by market capitalization, 13 are deeply engaged in chip design, including both fabless companies, which focus solely on design and outsource manufacturing — such as Nvidia, AMD, Qualcomm, and MediaTek — and design-led integrated device manufacturers (IDMs) like Intel, Samsung, Texas Instruments, and Analog Devices.

This dominance underscores how chip design remains the engine of innovation and market leadership in the industry. However, this central role has also brought growing challenges:

• The shift to advanced nodes (7nm and below) demands major design changes to handle increased circuit density and meet higher performance demands.
• Growing demand from AI and high-performance computing (HPC) lead to more complex chip designs, particularly due to compute-intensive GPUs and high-bandwidth memory.
• Rising competition from nontraditional players like Microsoft and AWS, who are now designing their own chips for AI applications, increases market pressure.
• Established companies like Nvidia, AMD, and Intel face added risk, having already invested billions in their design capabilities, now threatened by new entrants.
• Semiconductor design relies on a complex ecosystem of electronic design automation (EDA) tool providers — companies that develop software to design and verify complex semiconductor chips, intellectual property (IP) vendors, and software partners, requiring tight coordination across multiple stakeholders.
• The fragmented ecosystem slows down the design process, increases development costs, and raises the risk of integration issues and errors.

To maintain their competitive edge, established players must develop differentiated products — such as high-performance, energy-efficient, or application-specific chips — while also adopting emerging technologies like AI, generative AI, and agentic AI to stay ahead in a rapidly evolving landscape. These technologies can boost designer productivity, improve time-to-market performance, and reduce design and R&D costs, all while enhancing design quality. Embracing AI will be crucial for design companies to streamline processes, shorten cycle times, and manage rising costs effectively.

The role of AI in semiconductor manufacturing

Assessing the process from initial chip design to the final step of sending the design for manufacturing uncovers the operational challenges faced by design engineers, verification engineers, and project managers.

Most chip design activities are iterative and time-consuming, requiring repeated refinement. In addition, the verification stage often generates error logs with thousands of items that need to be addressed. Large semiconductor companies also manage extensive IP repositories which are collections of reusable circuit designs and components. Some companies maintain libraries with over 50,000 items, including both in-house and third-party designs. Furthermore, multiple distributed teams often collaborate on a single chip design, causing delays as engineers wait for responses to their queries.

However, using both generative AI and traditional machine learning (ML) technologies can automate these processes to save time and improve semiconductor yield.

Automated floor planning

Design engineers must optimize chip component layouts to meet multiple design constraints, such as performance, power consumption, and manufacturing costs. ML can help by analyzing previously validated floorplans for similar chips and using that data to create optimized layouts. Reinforcement learning (RL), a type of ML, can learn from past designs and iteratively improve floorplan layouts by considering design factors like signal integrity, power distribution, and thermal management. This approach automates and refines the layout process, helping to improve productivity and reduce time-to-market.

An example of this is Synopsys's application of ML in its IC Compiler II tool, where ML-driven macro placement technology predicts design metrics like congestion, wirelength, and timing. The tool learns from past results, exploring hundreds of floorplan options and selecting the most optimal layout.

Error log analysis for physical verification

The logs generated during chip verification are voluminous, often running into several gigabytes, and require human effort to review them and identify errors and warnings. ML algorithms can analyze the error text in reports and highlight key points to the user and also initiate actions by triggering workflow to the relevant stakeholders. This can help improve design productivity and reduce design cycle time.

Several tools already use ML to improve physical verification. For example, Siemens’ Calibre nmDRC speeds up early-stage checks by running localized analysis — targeting only parts of the chip layout that have changed or are prone to errors. This approach enables faster feedback, better prioritization of errors to fix and shorter debug cycles.

Copilot for design engineers

Chip design projects are complex and globally distributed, often involving multiple teams and specialists. Engineers frequently face delays when they depend on subject matter experts for resolving design and architectural queries. A generative AI-based design copilot can change that by offering real-time support, documentation assistance, and natural language interfaces to EDA tools. By integrating directly into engineers' workflows, such copilots can accelerate development cycles and improve design quality.

The Synopsys.ai Copilot is an example of this: It integrates across the Synopsys EDA stack to enhance productivity, learn from ongoing projects, and adapt to organizational best practices.

Generative AI-based IP search assistant

AI can help in the selection of relevant IP and its reuse — one of the time-consuming but key activities involved in the design of semiconductors. Design engineers search for the most relevant IP from large repositories to integrate them into new projects.

While much research has been done on efficient IP management and several platforms exist, AI integration into these systems has been slow due to concerns over data sensitivity, fragmented and inconsistent IP data, complex licensing constraints, and the technical challenges of integrating AI into existing design workflows.

An AI-based IP search system can:

• Scan product requirements documents containing detailed specifications — such as operating conditions, power, performance, and hardware/software configurations — to extract relevant search parameters.
• Use semantic search capabilities to return matching IP records based on context and meaning, rather than exact keyword matches, enabling more accurate and relevant results even when terminology varies.
• Identify potential risks related to IP infringement and licensing, helping designers avoid violations of export control regulations or agreements with third-party IP vendors.
• Provide integration time estimates based on historical data, giving project teams a clearer picture of how long IP integration could take.
• Offer insights into expected design quality by analyzing historical outcomes associated with similar IP usage.
• Assess potential cost implications for using specific IP blocks within a given project.

Considerations for AI adoption in chip design

AI is no longer a futuristic concept but a strategic part of semiconductor design. It can automate key processes like floor planning, verification, IP discovery, and support real-time collaboration, improving efficiency across the design lifecycle. To achieve this, companies need to identify where AI can add the most value, such as in error log analysis and design verification. They should also assess data availability to support proof-of-concept projects and integrate AI smoothly with EDA tools to avoid disrupting workflows.

It’s important to implement human-in-the-loop systems that provide engineers with insights while maintaining oversight and accountability. Continuous learning and iteration will help AI systems improve over time, offering more tailored solutions and speeding up design processes. This approach helps semiconductor companies make better use of AI within their existing operations.