Abstract
We present a non-invasive method for recognition of components in a digital CMOS integrated circuit (IC). We use a confocal infrared laser scanning optical microscope to collect multimodal images through the backside of the IC. Individual modes correspond to passive reflectivity measurements or active measurements, such as light-induced voltage alteration. The modes are registered and stored in a multidimensional data cube. We apply a machine learning algorithm using a binary representation to identify a variety of data structures from transistors to entire logic cells. Because of the compact representation, objects can be detected rapidly. We show that by increasing the number of imaging modes used to develop the descriptor, we can significantly increase recognition accuracy. The approach allows recognition of poorly resolved components, whose primary distinguishing features are below traditional optical resolution limits, and is general enough to be applied to multiple design processes. We believe this represents a significant step toward a fully non-invasive IC reverse engineering system.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Information Forensics and Security
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
