@article{ErozanHefenbrockBeigletal.2019,
  author    = {Ahmet Turan Erozan and Michael Hefenbrock and Michael Beigl and Jasmin Aghassi-Hagmann and Mehdi Baradaran Tahoori},
  title     = {Reverse Engineering of Printed Electronics Circuits: From Imaging to Netlist Extraction},
  series   = {IEEE Transactions on Information Forensics and Security},
  volume    = {15},
  publisher = {IEEE},
  address   = {New York},
  organization    = {IEEE Signal Processing Society},
  issn      = {1556-6013 (Print)},
  doi       = {10.1109/TIFS.2019.2922237},
  pages     = {475 -- 486},
  year      = {2019},
  abstract  = {Printed electronics (PE) circuits have several advantages over silicon counterparts for the applications where mechanical flexibility, extremely low-cost, large area, and custom fabrication are required. The custom (personalized) fabrication is a key feature of this technology, enabling customization per application, even in small quantities due to low-cost printing compared with lithography. However, the personalized and on-demand fabrication, the non-standard circuit design, and the limited number of printing layers with larger geometries compared with traditional silicon chip manufacturing open doors for new and unique reverse engineering (RE) schemes for this technology. In this paper, we present a robust RE methodology based on supervised machine learning, starting from image acquisition all the way to netlist extraction. The results show that the proposed RE methodology can reverse engineer the PE circuits with very limited manual effort and is robust against non-standard circuit design, customized layouts, and high variations resulting from the inherent properties of PE manufacturing processes.},
  language  = {en}
}