ObfusX: Routing Obfuscation with Explanatory Analysis of a Machine Learning Attack

This is the first work that incorporates recent advancements in "explainability" of machine learning (ML) to build a routing obfuscator called ObfusX. We adopt a recent metric—the SHAP value— which explains to what extent each layout feature can reveal each unknown connection for a recent ML-based split manufacturing attack model. The unique benefits of SHAP-based analysis include the ability to identify the best candidates for obfuscation, together with the dominant layout features which make them vulnerable. As a result, ObfusX can achieve better hit rate (97% lower) while perturbing significantly fewer nets when obfuscating using a via perturbation scheme, compared to prior work. When imposing the same wirelength limit using a wire lifting scheme, ObfusX performs significantly better in performance metrics (e.g., 2.4 times more reduction on average in percentage of netlist recovery).