Non-Invasive Recognition of Poorly Resolved Integrated Circuit Elements

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.

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