Semiconductor Engineering sat down to discuss the issues and challenges with machine learning in semiconductor manufacturing with Kurt Ronse, director of the advanced lithography program at Imec; Yudong Hao, senior director of marketing at Onto Innovation; Romain Roux, data scientist at Mycronic; and Aki Fujimura, chief executive of D2S. What follows are excerpts of that conversation.
SE: Machine learning is a hot topic. This technology uses a neural network to crunch data and identify patterns, then matches certain patterns and learns which of those attributes are important. We also have more advanced forms called deep learning. Is that correct?
Fujimura: Deep learning is a subset of artificial intelligence or AI. In machine learning, some people say that it’s a subset of AI. Some people say machine learning is a different computer science or a data analytics way of thinking. But it doesn’t matter either way. Deep learning is one particular kind of machine learning. It also has enhanced what machine learning can do. Is it a language? No, it is not a language. It’s an approach. Deep learning is a particular approach to software. In some ways, it’s automatic programming. Instead of a software engineer sitting down and writing code, deep learning involves an engineer sitting there manipulating what kind of a neural network to use and what kind of tuning he does to the neural network. But you also manipulate the data you give it. You train the neural network, so that the neural network that results is automatically programmed to do whatever it is that you want it to do. For example, you might want it to tell a cat from a dog, or a defect from a non-defect. You have an objective in mind that you want a deep learning neural network to do, and then you train it with data.
Hao: Pattern matching is part of machine learning. When you think about machine learning, you can say it’s a model. We build a predictive model that can map some inputs to some outputs. So we have new data come in, and then we can predict what the outputs would be. The applications include inspection, image processing, natural language processing and others.
SE: Machine learning isn’t new. AI and machine learning have been around for years. In the 1990s, for example, IBM presented a paper on ways to find defects in chips using an inspection system and early forms of machine learning. But the system was slow and inaccurate. Why did the early attempts fall short?
Hao: There are two reasons. One reason is computing power. At that time, it was not enough to support a complicated machine learning system. And second, the machine learning technology at that time was still in its infancy. The technology was not ready then. But over time, the semiconductor industry has seen vast improvements in computing power. This made it more likely that we can apply machine learning and artificial intelligence in the field. There is another factor, too. Everything is getting more complicated. The devices have become 3D. The complexity has grown exponentially. To just use physics to model everything is still possible, but it just may take a year for you to do it. Machine learning can make it much faster. So we’ve seen vast improvements in computing power and machine learning. That can benefit the semiconductor business.