Practical uses of AI in Electronics Design and Manufacturing

In our last post, we reviewed the current state of readiness for AI to do actual circuit design. We have a way to go. But that doesn’t mean that AI doesn’t offer significant efficiencies and labor-saving opportunities for the many steps along the path from initial concept to finished production units. Let’s see where it can be best employed.

Here's are some of the key opportunities:

1. Design Concepts:
   AI-based generative design may not yet be ready for fully workable circuit schematics, but it can help explore a wide range of design solutions that may not be immediately obvious to human designers. With so many different chips and packages available it’s virtually impossible for the human designer to know them all. AI, on the other hand, can. It may suggest components or approaches not otherwise considered that deserve deeper investigation.
   
   
2. Machine Learning for Simulation and Testing:
   AI is increasingly used to simulate and predict the behavior of electronic systems. This is one of the sweet spots for today’s AI. Example tools include Multisim and PLECS Machine learning models can analyze datasets from simulations and real-world performance to predict failure points or identify design flaws early in the process. These simulations can analyze factors like:
   
   • Thermal performance: Ensuring the circuit operates within safe temperature limits
   • Electromagnetic interference (EMI): Minimizing interference that could degrade system performance or affect surrounding systems
   • Reliability and stress testing: Identifying weak points in designs and suggesting improvements to enhance durability
   
   
3. Layout Optimization:
   AI has also made strides in optimizing the physical layout of integrated circuits. AI is realizing improvements in routing to minimize space, reduce power consumption, and optimize signal integrity in layouts. A couple tools claiming AI integration are Zucken and Flux
   
   
4. AI in FPGA Design:
   AI techniques are increasingly being used in the design of FPGA systems. AI algorithms can automate parts of the process, enabling more efficient and faster FPGA prototyping. One example tool to look at is Synopsys
   
   
5. AI in EDA Tools:
   Most of the popular Electronic Design Automation tools, such as those used for PCB design, are starting to incorporate AI to:
   • automate the placement of components on a board
   • optimize the routing of electrical traces between them
   • ensure minimal signal degradation
   • maximize space efficiency

AI’s ability to learn from vast libraries of past designs helps it recommend the best strategies. By automating the generation of optimized solutions and reducing the need for iterative testing and manual adjustments, generative design can significantly shorten design cycles and cut costs.

A caveat: with AI in the steep part of the hype curve, everyone wants to claim that their product is incorporating it. Some of these claims are likely to be still in the ‘aspirational’ phase. While these tools (with or without AI) significantly increase the efficiency of the design process, challenges remain in terms of integrating AI fully into traditional design workflows and ensuring that the end designs are both reliable and safe for use in real-world applications.

Nevertheless, AI tools will continue to rapidly mature. You can expect that generative design will become increasingly ubiquitous in electronics, pushing the boundaries of what is possible in creating next-generation devices. Engineers will be able to focus more on overseeing the AI-generated proposals rather than creating every part of the design from scratch. Stay tuned.

Thanks for reading.

So, Are you using AI for electronics design?