Efficient Integrated Circuits Characterization Through Computer Vision Assistance

Industry tends to optimize accuracy and time efficiency of every process analyzing its constraints and limits. Several tasks requiring high precision and reproducibility must be automated. In the context of secure Integrated Circuits (ICs) characterization, tasks such as power analysis are commonly automated. However, very few automations exist for tools calibration, while recent characterization schemes encounter mechanical constraints. Computer vision, flexible tool used in various fields, gives opportunities to address these constraints. In the case of laser fault injections, several positioning adjustments are required to ensure a maximal energy transmission in a targeted point. An accurate focalization of the laser beam is reached by using an autofocus system. Such a system is obtained by analyzing the camera view of the IC in the time-frequency domain. Whatever the method to disturb an IC, every secure characterization should be reproducible. By exploiting computer vision assistance, fault injection can be automated by mixing vision techniques to build a full or partial view of an IC and automatically identify the targeted IC and focus the perturbation on a chosen pattern in the image. A reliable pattern detection is implemented by studying spatial consistency of image's remarkable features represented by graphs. This paper presents several computer vision techniques to address above problems.