Modern electronics manufacturing isn’t possible without starting with PCB design because this is the foundation or blueprint that guides the circuit-building process. But as PCBs become increasingly complex and have multiple layers, circuit design has become a high-attention skill, something that is necessary to prevent costly errors later on. AI is making the design process a little easier though. Most PCB design software applications are integrating AI to make the process more efficient, reduce errors, and boost innovation in ways that we’ll look at in a bit. First, let’s get a general understanding of the PCB design process workflow, then see how AI is lending a hand.
The PCB Design Process Workflow
- Capturing the Schematics. Design engineers create the schematic diagram, which shows the connection between components as represented by standardized lines and symbols.
- Component Placement. The next process is to create the PCB layout, which highlights the component arrangement on the surface. This placement must ensure optimal circuit performance and manufacturability.
- Trace Routing. The traces that were represented using lines in the schematic diagram are drawn to connect the placed components, and they can be routed through multiple layers interconnected by vias and plated through holes.
- Design Rule Checking. Each PCB design is checked against a set of predefined rules to ensure it meets the required standards (internal and industry) as a form of quality checking.
- Simulation and Testing. This last step ensures the PCB is functional while still in virtual form to prevent the manufacturing of faulty prototypes.
How AI Lends a Hand in PCB Design
The five tasks above can take a lot of time if the PCB has a complex design, but AI cuts the design time and simplifies the challenging tasks in the following ways.
Automating Component Placement
Component placement is not as easy as it sounds because design engineers have to think about optimizing the available real estate while reducing or eliminating noise/interference. AI algorithms that are trained on various space optimization techniques and interference reduction can help automate component placement by doing the following.
- Analyzing the schematic
- Factoring in signal integrity and thermal requirements
- Proposing various optimized layouts with components placed for manual review
These tasks can save design time and reduce the required iterations by providing the designers with workable layouts to start with.
Component Selection with Supply Chain Integration
The supply chain is a critical factor to consider when designing PCBs because the placed components must be available to assemble the board later. So modern design software integrates with real-time supply chain data to ensure all the placed components are available in the market. AI can also recommend the best components if the ones the designers want to use are heading towards obsolescence.
Providing Intelligent Routing
Routing involves interconnecting the placed components using lines that represent the traces. High-density PCB designs are incredibly complex to route because they need multiple traces packed in tiny spaces. Some of the issues that come up in such layouts include impedance mismatching and electromagnetic interference, all of which result in signal degradation.
AI algorithms can determine the most suitable and efficient paths for copper traces while reducing signal degradation, resulting in faster and more reliable signal routing in multilayer PCBs.
Assisting in Design Rule Checking (DRC)
The traditional way of performing DRC is to check for errors at the end of the design process, which can leave designers with the arduous task of rectifying deeply embedded mistakes. But with AI models trained on past DRC data, design software can highlight errors like thermal hotspots on the fly, making it possible to detect faults early and save time.
Signal Integrity and Thermal Analysis
Balancing between thermal performance and signal integrity is key in high-performance PCBs, and the best way to determine these is by doing simulations. AI can run simulations on the PCB design to check the signal paths and thermal distribution, which will show weak points and areas of concern that require rectification.
Providing Generative Designs
Most people are familiar with the term generative AI when it comes to large language models. But the same can be applied to PCB design, where AI can provide several PCB layouts based on entered parameters, such as a set of constraints, manufacturability, cost, and performance. This helps to hasten the innovation cycles to produce better, high-performance designs.
Benefits of AI in PCB Design
- Increased Production Efficiency: The primary benefit of using AI in PCB design is to increase production efficiency. AI automates repetitive tasks leaving designers to focus on design creativity and problem-solving.
- Faster Time-to-Market: Since AI accelerates the design process, it enables electronics companies to launch products to the market faster.
- Reduced Errors: By detecting potential circuit issues early, AI reduces or eliminates the production delays associated with costly reworks.
- Enhanced Innovation: The generative design aspect of AI can help to bring forth novel solutions or new ways of eliminating PCB issues that cause signal degradation.
- Cost Savings: By optimizing PCB designs, AI keeps material costs low, streamlines the supply chain, and enhances manufacturability, making the final PCB more affordable to produce.
The Future of AI in PCB Design
AI is just in its infancy, so the future of PCB design will only get better. The technology is expected to drive new trends and expand existing ones by providing the following.
- Digital Twins: A digital twin is a virtual replica of an actual PCB that enables optimization and testing of the circuit board before PCB manufacturing commences. This reduces prototyping costs.
- Better Team Collaboration: AI is expected to boost real-time collaboration between distributed teams when developing complex PCB designs for futuristic electronic products.
- Fully Automated PCB Designs: AI might also be able to handle the PCB design process autonomously in the future, eliminating human oversight in this initial stage or the entire production process.
- Better Sustainability: As AI gets better, it is bound to optimize PCB designs further for energy efficiency and to consume minimal materials during production, resulting in lower manufacturing and assembly costs.
Conclusion
AI has already positively impacted PCB design in significant ways and will only make the process more efficient, innovative, and precise in the future. So designers must keep up with the times, adopting tools that integrate AI into the design process to make it more efficient. PCB manufacturers should also follow suit because design and fabrication are heavily intertwined. Companies like OurPCB provide both services and have integrated AI into their business to hasten delivery and pass on the benefits described above to their customers. This is a case of shape up or ship out, and OurPCB has already shaped up.
