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14 DESIGN007 MAGAZINE I AUGUST 2023 it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrin- sic structures in input data. Choosing the right algorithm can seem over- whelming; there are dozens of supervised and unsupervised machine learning algorithms, and each takes a different approach to learn- ing. ere is no best method or one size fits all. Finding the right algorithm is partly just trial and error but is also based on the recogni- tion of patterns learned, and experience with similar designs. e algorithm selection also depends on the technology used (e.g., DDR4), frequency, and rise time of the signal. e biggest issue in training AI with PCB images is the availability of data. ChatGPT had access to the unlimited wealth of information available on the internet, however, PCB mod- els only have access to the images and data fed into the machine learning model which, by comparison, is extremely limited. One way around this would be to create an open-source global repository for PCB layout images and databases that have endorsed functionality. However, convincing customers to share their designs may be difficult as most do not wish to publicly expose their intellectual property. Key Points • ere are many ways to achieve the same goal, but some ways are better than others in the context of system integrity or a spe- cific application. • Reducing the number of iterations and, hence, the design cycle time by using AI can be extremely cost-effective. • Shorter interconnects and reduced cross- overs are essential for both chip and PCB layout but critical routing incorporating signal integrity and flight time requirements is of greater importance for the PCB. • Currently, EDA tools use algorithms to control auto-placement and routing. • With the advent of AI, machine learning applies artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Figure 2: Machine learning model for PCB layout.