Judging by the presentations at the 2018 Symposium on VLSI Technology, held in Honolulu this summer, the semiconductor industry has a challenge ahead of it: how to develop the special low-power hardware needed to support artificial intelligence-enabled networks. To meet society's needs for low-power-consumption machine learning (ML), "we do need to turn our attention to this new type of computing," said Naveen Verma, an associate professor of electrical engineering at Princeton University." While introducing intelligence into engineering systems has been what the semiconductor industry has been all about, Verma said machine learning represents a "quite distinct" inflection point. Accustomed as it is to fast-growing applications, machine learning is on a growth trajectory that Verma said is "unprecedented in our own industry" as ML algorithms have started to outperform human capabilities in a wide variety of fields. Faster GPUs driven by Moore's Law, and combining chips in packages by means of heterogenous computing, "won't be enough as we proceed into the future.

At the 2017 Advanced Process Control (APC 2017) conference, several companies presented implementations of deep learning to find transistor defects, align lithography steps, and apply predictive maintenance. The application of neural networks to semiconductor manufacturing was a much-discussed trend at the 2017 APC meeting in Austin, starting out with a keynote speech by Howard Witham, Texas operations manager for Qorvo Inc. Witham said artificial intelligence has brought human beings to "a point in history, for our industry and the world in general, that is more revolutionary than a small, evolutionary step." People in the semiconductor industry "need to take what's out there and figure out how to apply it to your own problems, to figure out where does the machine win, and where does the brain still win?" At Seagate Technology, a small team of engineers stitched together largely packaged or open source software running on a conventional CPU to create a convolution neural network (CNN)-based tool to find low-level device defects. In an APC paper entitled Automated Wafer Image Review using Deep Learning, Sharath Kumar Dhamodaran, an engineer/data scientist based at Seagate's Bloomington, Minn.