Recently, an engineer shared with EEWorld:
"Many of the software tools I use are integrating AI, even EDA simulation and verification tools. So, which PCB design software providers are actively embracing AI, and what do engineers think about it?"
This article explores these questions, highlighting key industry players integrating AI into PCB design and examining how engineers perceive AI's role in the field.
Siemens is making significant investments in AI across multiple domains, from PCB design to autonomous driving, smart factory management, and smart city infrastructure. Its EDA division has introduced the Catapult HLS AI toolkit, designed to help customers develop AI/ML-based accelerators for edge applications, reducing time to market.
Cadence is revolutionizing PCB design with Allegro X AI, which accelerates the design process by more than 10x. Its key AI-powered features include automated layout, power plane creation, and critical network routing. OrCAD X leverages Allegro X AI for schematic planning and automated component placement, addressing signal integrity, power integrity, and thermal management.
Cadence ensures AI enhances, rather than replaces, traditional computational algorithms and automation methods. Allegro X AI is trained on both successful and failed designs, using data for optimization rather than judgment. Additionally, Cadence Cerebrus AI helps chip designers create faster, more efficient, and cost-effective semiconductor solutions.
KiCad’s layout design is now integrated into the CELUS design platform, enabling engineers to streamline complex workflows without additional symbols or footprint setup. Some engineers are also experimenting with AI-powered workflows using Cursor AI + SKiDL + KiCad, leveraging AI for automatic circuit code generation and enhanced efficiency.
Huawei Cloud’s pEDA Space platform provides an end-to-end PCB design and manufacturing toolchain with cloud-based collaboration, featuring AI-powered layout and routing, real-time teamwork, automated error detection, cloud-based viewing of complex PCBs, and pre-trained AI models for circuit design.
Pi-CES, leveraging Siemens EDA PADS and its AI model, developed a "3D Five-Tier" design system with AI-driven libraries, seamless Altium and Cadence compatibility, automated routing via SailWind, and integrated PCB manufacturing data to reduce prototyping iterations.
Recom is integrating its 30,000-component catalog into the Celus knowledge database, enabling AI-driven component selection and optimization based on project requirements. AI also assists in modifying layouts, adding polygons or copper planes, and adjusting design constraints.
This UK-based AI startup is offering free PCB design services. Engineers can upload .dsn schematic files and receive AI-generated PCB layouts within 24 hours. DeepPCB, InstaDeep’s reinforcement learning-powered software, automates PCB routing for up to four-layer boards and integrates with KiCad.
The introduction of AI is revolutionizing PCB layout and design. While early PCB layout tools offered little improvement over manual routing, rule-based automation has since accelerated the process. However, complex multi-layer designs, 3D stacking, and heterogeneous integration still require expert human intervention.
AI can optimize PCB design by:
- Enhancing layout automation with deep learning algorithms
- Improving signal integrity and reducing interference
- Streamlining component selection and placement using cloud databases
- Predicting power and thermal performance based on AI-driven simulations
While current EDA tools generate current density maps, they still require human expertise for trace width and aspect ratio decisions. However, integrating AI-powered power estimation could enable real-time layout optimization.
AI-driven PCB tools remain costly, limiting adoption among small and mid-sized businesses. However, as technology matures, AI is expected to become a standard feature, increasing both efficiency and design quality.
Opinions on AI in PCB design are mixed. Some believe that AI may struggle to replace human engineers due to the industry's cost structure:
"PCB design has been a stable-priced service for years—can AI really compete with low-cost human labor?"
Others argue that PCB design is fundamentally rule-based and AI is a natural fit for automation.
One engineer likened PCB layout to HDL-based chip design, highlighting a key difference: while chip logic design follows a predefined structure with power, ground, and clock signals already connected, PCB design varies based on the designer’s approach, influencing signal integrity, manufacturability, and even aesthetics.
AI has the potential to automate key aspects of PCB design, including component placement, routing optimization, DFM checks, and rule-based constraint enforcement, streamlining the process while ensuring design consistency.
Many believe AI will enhance rather than replace PCB engineers, as it can rapidly generate multiple design options but still requires human expertise for requirement gathering, design validation, and production oversight. Engineers will remain essential in ensuring design accuracy, meeting specifications, and overseeing manufacturing and testing processes.
Some speculate that AI-driven PCB tools could lead to industry monopolization, with a few elite companies dominating AI-powered EDA software, leaving smaller firms with limited market share.
AI is undoubtedly transforming PCB design, offering faster, more optimized layouts and reducing human workload. However, challenges remain in cost, adoption, and market consolidation. As AI technology advances, engineers and AI tools are likely to coexist as collaborative partners, rather than direct competitors.
