In the past decade, a lot of progress has been made in the design and
evaluation of logic locking; a premier technique to safeguard the integrity of
integrated circuits throughout the electronics supply chain. However, the
widespread proliferation of machine learning has recently introduced a new
pathway to evaluating logic locking schemes. This paper summarizes the recent
developments in logic locking attacks and countermeasures at the frontiers of
contemporary machine learning models. Based on the presented work, the key
takeaways, opportunities, and challenges are highlighted to offer
recommendations for the design of next-generation logic locking.

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