AI in PCB Design: It’s Already Here

How tools are advancing the speed with which highly computational tasks are performed.

The integration of artificial intelligence (AI) into printed circuit board (PCB) design is no longer a vision for the future; it is a reality. Advanced electronic design automation (EDA) tools now incorporate AI-driven solutions, providing a competitive edge for those ready to embrace this transformative technology. Yet, despite its availability and potential, many remain reluctant to adopt AI.

Given the current shortage and fierce competition for talent, the number of skilled engineers entering the industry is simply not enough, a workforce gap that continues to grow. Companies are addressing this issue as best they can. Current engineers are forced to do more with less, as well as cross-pollinate in disciplines outside their specialty in the quest for project success. New engineers must get up to speed faster, assuming more tasks while learning on the fly, which adds stress and concerns to the project as well as to each individual team member.

Today’s advanced feature-rich EDA tools address these concerns and now include AI-driven solutions to further optimize resources and processes.

Adoption Reluctance

Despite its advantages, AI in PCB design faces skepticism for several reasons. Let’s unpack them.

1. Trust issues. Many engineers and designers question whether AI can match the quality of human work. This hesitation stems from a natural skepticism toward new technologies that could disrupt established workflows.
2. Job security concerns. Some fear that AI might replace human designers, overlooking the potential for AI to complement their skills. Rather than taking over jobs, AI excels at meticulous, time-consuming tasks, allowing designers to focus on more innovative aspects of their work. AI enhances the engineer’s capability to be a better engineer, innovate more, and enables them to produce higher quality complex products, faster and at a lower engineering cost.
3. Company-specific challenges.

• • Data privacy. Companies worry that permitting AI to learn from their internal intellectual property (IP) could inadvertently share proprietary knowledge with tool vendors or competitors. (In certified, secured solutions, however, companies can continue to have every bit of control as before.)
  • Cost uncertainty. The financial and time investment required to train AI models remains a concern for organizations. Questions about the cost-effectiveness of AI tools persist, especially for smaller teams.

PCB Design Efficiency

AI excels at tasks that involve data analysis, pattern recognition and automation, making it a powerful tool for various stages of PCB design. Its ability to process vast amounts of information and identify optimization opportunities is transforming how engineers approach circuit board development.

One major impact of AI is in data-driven decision-making. By analyzing large volumes of data from previous designs, AI can identify trends and recommend optimized layouts. Leveraging historical data helps engineers avoid errors, improve circuit performance, and reduce design iterations, ultimately leading to more efficient and reliable PCB designs.

Another key advantage of AI is its ability to automate rule-based tasks. Traditional PCB design involves repetitive and time-consuming processes, such as component placement and routing. AI can handle these tasks automatically, minimizing manual intervention and allowing engineers to focus on innovation and complex problem-solving.

AI also plays a crucial role in predictive analysis for design optimization. AI-driven algorithms can anticipate potential design flaws before they become critical issues. By simulating different scenarios, AI helps designers optimize their boards for performance, power efficiency, and manufacturability, ensuring a more robust final product.

Furthermore, AI accelerates verification and manufacturing preparation. As PCB designs become more complex, these stages are increasingly time-consuming. AI streamlines the process by identifying potential manufacturing defects early, reducing verification cycles, and ensuring a smoother transition to production. This efficiency helps manufacturers bring products to market faster while maintaining high-quality standards.

What AI Can Do – And What It Can’t

AI has made significant strides in assisting engineers with PCB design, but it is still far from fully replacing human designers. One of the primary reasons is the complexity and nuance of the design process, which requires deep engineering judgment, creativity and experience that AI struggles to replicate. While AI tools can someday optimize layouts, suggest component placements, and even generate basic routing solutions, they cannot fully understand the tradeoffs involved in design decisions. Factors such as electromagnetic interference (EMI), thermal management and manufacturability often require human expertise to evaluate and resolve effectively.

Another challenge AI faces in PCB design is the need for industry-specific knowledge and adaptability. Different applications, such as high-speed digital circuits, RF designs, or power electronics, have unique requirements that AI cannot easily generalize across. Designers often need to interpret vague or incomplete specifications, collaborate with mechanical engineers, and make adjustments based on real-world constraints – something AI is not yet capable of doing independently. AI models also rely heavily on training data, and while they can assist with designs based on existing patterns, they struggle with novel concepts or cutting-edge technologies where past data are limited or nonexistent.

Moreover, AI-generated PCB layouts still require human oversight and validation. Errors in design rules, unexpected signal integrity issues or thermal challenges may not be immediately apparent to AI but can have severe consequences if overlooked. Experienced engineers can spot potential problems before they manifest in physical prototypes, ensuring the reliability and performance of the final product. Until AI can reason through complex design tradeoffs, fully understand functional requirements, and independently validate its own designs with a high degree of reliability, human PCB designers will continue to lead the design process.

Implementation

Taking into account the power of AI and its limits, tool developers are integrating AI algorithms that solve specific challenges and increase productivity. Here are three use cases.

1. Analytical AI. In the past, engineering analyses and simulations of complex designs could take days or even weeks to complete, if they were feasible at all. Today, with the power of analytical AI, these tasks require significantly less time. Some tools enable advanced design optimization, making it practical to evaluate a vast number of simulation cases while using only a fraction of the computational resources needed by traditional methods. Fast, efficient optimization is crucial when exploring large design spaces with minimal simulations. Analytical AI allows engineers to leverage high-speed design analysis and simulation tools to automate optimization by defining constraints such as processing power, target evaluations and evaluation limits. By specifying design variables and optimization goals, the AI engine efficiently navigates the design space and recommends the best solutions. Additionally, it creates surrogate models to accelerate future analyses, further enhancing efficiency and decision-making in the engineering process.

AI systematically explores the PCB design space, refining iterations based on performance goals such as trace width, thermal constraints and signal integrity while leveraging AI-driven surrogate models to accelerate future analyses and reduce computational costs without sacrificing accuracy. HyperLynx DSE (HL-DSE), for instance, enhances this process by minimizing required simulations through techniques like simple iteration, swept-parameter analysis, and response surface analysis. For product managers, this translates to reduced time-to-market, increased first-pass success rates, and a competitive edge in high-performance electronic system development.

2. Predictive AI. Predictive AI enhances user experience (UX) by intelligently anticipating commands during the PCB design process. This user interface (UI) command prediction is an on-the-fly machine learning model that analyzes the user’s command usage during the PCB design process and predicts the next command likely to be needed based on the last command used. It can leverage the seed model or a company’s design team expert can train a new model.

Engineers benefit from a workflow that automatically suggests the next command based on recent actions, minimizing manual tool navigation and streamlining productivity. The system adapts to each user’s working style and permits organizations to train custom AI models tailored to company-specific workflows.

With seamless integration, this predictive AI is available across certain next-generation EDA tools, enabling consistent AI-powered user experiences across different PCB and system design tasks. For product managers, this translates to higher efficiency, standardized best practices and a consistent UX.

3. Generative AI. Generative AI revolutionizes component selection by enabling engineers to interact with smart, AI-powered data sheets. Instead of manually searching through lengthy PDFs, users can query component specifications and characteristics in natural language questions and receive precise, context-aware responses. User-friendly chatbot interface and prompt templates help make critical component selection decisions faster.

The chatbot interface provides pre-built prompt templates, allowing engineers to retrieve critical component data instantly. For product managers, this feature accelerates component selection, improves BoM accuracy, and ensures AI-powered assistance is available across all teams, driving efficiency in procurement and design validation.

Each of these use cases underscores the unique value of AI: its ability to process vast amounts of data, learn from patterns, and apply insights that would be impractical or impossible for humans alone to replicate. Overall, AI-driven UX empowers engineers with automation and real-time insights while providing product managers with faster product development cycles, improved team efficiency, and consistent design workflows. This intelligent AI integration not only enhances usability but also ensures organizations stay ahead in the competitive landscape of electronic system design.

The Path Forward

The evolution of PCB design relies on the adoption of AI-driven tools. By overcoming trust issues, addressing concerns about IP security and demonstrating cost-effectiveness, companies can harness AI’s potential to stay competitive in an increasingly demanding landscape. AI is not here to replace designers; it is here to empower them, permitting organizations to meet their goals while navigating the challenges of modern design.

In a world of shrinking workforces and rising complexity, the question is no longer whether AI should be part of the PCB design process, but how soon companies will embrace it to maintain their edge.

Stephen Chavez is a senior printed circuit engineer with three decades’ experience. In his current role as a senior product marketing manager with Siemens EDA, his focus is on developing methodologies that assist customers in adopting a strategy for resilience and integrating the design-to-source Intelligence insights from Supplyframe into design for resilience. He is an IPC Certified Master Instructor Trainer (MIT) for PCB design, IPC CID+, and a Certified Printed Circuit Designer (CPCD). He is chairman of the Printed Circuit Engineering Association (PCEA); stephen.chavez@siemens.com.