Machine Learning… Everywhere | Machine Learning - SemiEngineering

How ML can enable self-optimizing tools that look for DRC hotspots, EM/IR distribution, and more, notes Stelios Diamantidis, director of AI products and research at Synopsys.

Photo: JumpStory

AI is transforming the world around us, creating an avenue to innovation across all sectors of the global economy. Today, AI can interact with humans through natural language; identify bank fraud and protect computer networks; drive cars around city streets; and play complex games like chess and Go. Machine-learning is offering solutions to many complex problems around us where analytical solutions may be too expensive or practically impossible. How about chip design? Can ML offer solutions to key problems in semiconductor engineering?

A deluge of design challenges
Over the years, the EDA industry has offered many solutions in the modeling and design creation of complex systems. Most design problems in EDA are NP-hard; there are simply no polynomial-time algorithms to solve these problems and hence an optimal solution cannot be identified analytically. Today’s EDA systems are finding it difficult to keep up with advanced process node requirements due to a deluge of new design challenges (figure 1).

Figure 1

To make things worse, these requirements are interdependent and need to be considered concurrently across multiple planes of design optimization. The actual application and techniques used depend highly on each specific problem space. How does one prepare a general solution for a specific problem when there is limited access to the design environment?
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Source: SemiEngineering