Designing integrated circuits typically requires years of human expertise. Recent work set AI to the task with surprising results.
What’s new: Emir Ali Karahan, Zheng Liu, Aggraj Gupta, and colleagues at Princeton and Indian Institute of Technology Madras used deep learning and an evolutionary algorithm, which generates variations and tests their fitness, to generate designs for antennas, filters, power splitters, resonators, and other chips with applications in wireless communications and other applications. They fabricated a handful of the generated designs and found they worked — but in mysterious ways.
How it works: The authors trained convolutional neural networks (CNNs), given a binary image of a circuit design (in which each pixel represents whether the corresponding portion of a semiconductor surface is raised or lowered), to predict its electromagnetic scattering properties and radiative properties. Based on this simulation, they generated new binary circuit images using evolution.
- The authors produced a training set of images and associated properties using Matlab EM Toolbox. The images depicted designs for chip sizes between 200x200 micrometers (which they represented as 10x10 pixels) and 500x500 micrometers (represented as 25x25 pixels).
- They trained a separate CNN on designs of each size.
- They generated 4,000 designs at random and predicted their properties using the appropriate CNN.
- Given the properties, the authors used a tournament method to select the designs whose properties were closest to the desired values. They randomly modified the selected designs to produce a new pool of 4,000 designs, predicted their properties, and repeated the tournament. The number of iterations isn’t specified.
Results: The authors fabricated some of the designs to test their real-world properties. The chips showed similar performance than the CNNs had predicted. The authors found the designs themselves baffling; they “delivered stunning high-performances devices that ran counter to the usual rules of thumb and human intuition,” co-author Uday Khankhoje told the tech news site Tech Xplore. Moreover, the design process was faster than previous approaches. The authors’ method designed a 300x300 micrometer chip in approximately 6 minutes. Using traditional methods it would have taken 21 days.
Behind the news: Rather than wireless chips, Google has used AI to accelerate design of the Tensor Processing Units that process neural networks in its data centers. AlphaChip used reinforcement learning to learn how to position chip components such as SRAM and logic gates on silicon.
Why it matters: Designing circuits usually requires rules of thumb, templates, and hundreds of hours of simulations and experiments to determine the best design. AI can cut the required expertise and time and possibly find effective designs that wouldn’t occur to human designers.
We’re thinking: AI-generated circuit designs could help circuit designers to break out of set ways of thinking and discover new design principles.