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Logic locking protects the integrity of hardware designs throughout theintegrated circuit supply chain. However, recent machine learning (ML)-basedattacks have challenged its fundamental security, initiating the requirementfor the design of learning-resilient locking policies. A promising ML-resilientlocking mechanism hides within multiplexer-based locking. Nevertheless, recentattacks have successfully breached these state-of-the-art locking schemes,making it ever more complex to manually design policies that are resilient toall existing attacks. In this project, for the first time, we propose theautomatic design exploration of logic locking with evolutionary computation(EC) -- a set of versatile black-box optimization heuristics inspired byevolutionary mechanisms. The project will evaluate the performance ofEC-designed logic locking against various types of attacks, starting with thelatest ML-based link prediction. Additionally, the project will provideguidelines and best practices for using EC-based logic locking in practicalapplications.

AutoLock: Automatic Design of Logic Locking with Evolutionary Computation