  Hardware trojan detection methods, based on machine learning (ML) techniques,
mainly identify suspected circuits but lack the ability to explain how the
decision was arrived at. An explainable methodology and architecture is
introduced based on the existing hardware trojan detection features. Results
are provided for explaining digital hardware trojans within a netlist using
trust-hub trojan benchmarks.
