MLConnect: A Machine Learning Based Connection Prediction Framework for Error Correction in Recovered Circuit

Integrated Circuit (IC) verification is of paramount importance to the security of IC. The success of circuit verification largely depends on the correctness of the recovered circuit netlist from Scanning Electron Microscopic (SEM) images. Due to imperfections in imaging process and feature extraction process, the recovered circuit netlist usually contains connection errors. The corrections of these errors require tedious manual tracing of metal lines or are sometimes impossible due to the corrupt regions in SEM images. In this work, we perform error correction based on a connection heuristic in circuit. We propose MLConnect, a machine learning based connection prediction framework that captures the probabilities of gate connections in circuits. We further propose a post-processing technique to recover circuit connections based on gate connection probabilities and circuit rules. Our results show that the proposed MLConnect successfully recovered 80.87% of gate connections in erroneous circuits from ISCAS-85 benchmark suites. Our method can largely automate the process of circuit recovery.