AI placement in PCB design is both possible and could be a road that brings designers into a new era of innovation.
Artificial Intelligence(AI) has been available in most EDA tools including PCB layout for some time now. Though the potential for machine learning exists in EDA, PCB designers have been slow to adopt a technology that currently auto-places and auto-routes for silicon. Most PCB designers manually route and design their boards, a time consuming and intricate process.
As early as the 1980’s, neural networking was an established theoretical concept in EDA. By the 1990’s, there were tools in place that could use the concepts.
Neuroroute – a product based on neural networks – was given 50 to 60 human-made PCB designs. These designs were relayed to an AI routing engine that used supervised machine learning to create an auto-router that makes decisions like a human.
Neuroroute paved the way for modern topological techniques. For example, it mapped out board space in triangles or other non-rectangular polygons, allowing for more efficient channel utilization and any-angle routing. This was a big development over the rectilinear “shape-based” routers of the time, but these early implementations lacked the computing power as well as the quantity and depth of training sets to be useful.
Part of my role as a product manager in EDA is to gain feedback from PCB designers and understand their needs and uses for our tools. I hear three common reasons why designers don’t like to use auto-routing:
- “It makes a mess”
- “The software doesn’t work.”
- “It may reduce the need for human PCB designers.”
What designers really mean when they say that auto-routing “makes a mess” is that the finished PCB doesn’t look aesthetically pleasing after the auto-routing process. PCB designers are a group that takes pride in the utility of their designs and their designs’ ornamental value.
Designers who say the software doesn’t work are actually referencing the default settings on their design software. When auto-routing is paired with their designs, the software’s default settings often complete anywhere from 50 to 70 percent of the design before giving up.
Luckily, this is an easy problem to solve. It may just be that designers need to tweak the default settings, constraints and design rules to match their designs. EDA software makers can improve out-of-the-box functionality by readjusting these defaults.
Finally, it has been my experience that many PCB designers are skeptical of automation in PCB design because it may one day replace designers and leave them out of a job. To this I say that designers are more involved in the design process than ever. Humans still need to add flare and artistic integrity to each design.
Today, there is enough computing power and internet capacity to enable this technology. AI libraries and infrastructure enable a streaming data approach.
Given permission by users, AI could be doing deep learning of the PCB design processes right now. And this wouldn’t replace the need for human designers –it would only empower them to create the best designs possible, like Iron Man putting on his special suit.