How do you use AI and ML for hardware design?

Hardware design can be classified into two categories. First is the IC design and second is the Electronics design using the ICs to design the Printed Circuit boards. Both of them need extensive use of tools. AI/ML helps if the tools use these two. The trouble is the design rules that primarily drive the complex design tend to be closely held by the designers. For ML/AL you need these data to build into the tools. But the situation is orthogonal and needs collaboration with different companies to make them share the closely held secrets to help the community. This is going to be a big challenge. If this can be achieved the design time and revisions can be drastically reduced to save time and money.

Achieving a successful hardware design on the first attempt requires a comprehensive understanding, a robust design process, and significant effort. Artificial Intelligence (AI) and Machine Learning (ML) can significantly aid hardware designers by swiftly familiarizing them with protocol requirements and expediting the selection of appropriate components. Additionally, AI and ML can streamline the layout process by automatically routing signals and creating planes. This automation will free up the bandwidth of hardware designers, enabling them to develop products with a faster time to market.

From my 3 decades plus experience in hardware design. Primary challenges have been in engineering the designs for large-volume production (both in VLSI and in PCBAs). CAD tool vendors create generic rules for checking the design as the design with tools progresses. However, experience is needed for High Speed, High power, and EMC/EMI-compliant designs which most companies do hold as their family silver and rarely made public. HP was on organization that had one of the best design guidelines that can be integrated into tools. I used those 200+ page books when I worked for a company that was JV with HP. If these kinds of golden design rules which are public can be brought into the ML/AI framework the efficiency in design will go up multifold

Assuming we have a set of tools that use globally available knowledge and use them as the basis for ML/AI-enabled design tools. advantages are; 1. No need for experienced engineers to be involved because they have the "Tribal Knowledge" which is closely held. 2. Avoiding an error during the design reduces the development time and reduces the number of revisions above all the development costs. 3. for example if the semiconductor vendors have a repository of real-time availability many of manufacturing delays can be avoided. But this is easier said than done with Semiconductor companies reducing the inventory holds prices vary depending on desperation! 4. Todays education engineers lack basic and ML/AI can help in bridging that gap

AI/ML usage will depend on the tools vendors and how they integrate ML/AI. ML implementation calls for training and we need data for that. However, engineering a product is an art and many OEMs keep the rules they use as Intellectual Property or secret. Tool vendor-provided ML/AI can solve generic and mundane issues, but not challenging issues. So challenge will be how complex issues that differentiate the products are brought into the framework of the tools. Till these issues are addressed ML/AI will have only basic success.