Autofocus (AF) is a widely investigated subject in the fields of natural scene images, industrial assembly and biologic microscopy. This paper proposes a new effective AF method for infrared (IR) microscopy in the context of the Integrated Circuit industry (IC). The proposed method operates in the wavelet domain using a custom orthogonal wavelet for the 2D Discrete Wavelet Transform (DWT). The quality criterion of our AF algorithm relies on the standard deviance of the DWT coefficients, computed per subband and per level. Tested on several optical magnifying lenses, our method is robust time-efficient, and usable on-the-fly in the IC location system.

This paper proposes an innovative autofocus method to ensure the image of an integrated circuit is correctly in focus under an infrared microscope. It discusses the difficulties inherent to the optical system and explores several inefficient methods used for natural scenes. It will also present a Focus Metric based on POlynomial Decomposition (FMPOD) adapted to our context. This approach relies on analyzing the projection of images on an orthonormal polynomial basis. Its robustness is tested using different magnifying lenses in addition to multiple distortions. In conclusion, we will demonstrate how this novel approach outperforms existing methods related to our work environment.

This paper addresses the detection of noisy structures in the context of infrared microscopy using labeled undirected graph matching. The selection of robust features as labels is determinant in this case of study, where hard conditions are dealt with: few relevant topological information, a potentially huge number of outliers and low contrasted images resulting in noisy graphs. Firstly, the image texture is reliably caught through a scale, rotation and intensity invariant histogram of oriented gradients. Secondly, in structures presenting numerous symmetries, the graph shape is locally registered while discriminated using weight from a super-increasing series. Coupled to the flexibility of the graph tensor product-based similarity metric, the matching framework achieves satisfying results.

In this paper, we present an infrared microscopy based approach for structures’ location in integrated circuits, to automate their secure characterization. The use of an infrared sensor is the key device for internal integrated circuit inspection. Two main issues are addressed. The first concerns the scan of integrated circuits using a motorized optical system composed of an infrared uncooled camera combined with an optical microscope. An automated system is required to focus the conductive tracks under the silicon layer. It is solved by an autofocus system analyzing the infrared images through a discrete polynomial image transform which allows an accurate features detection to build a focus metric robust against specific image degradation inherent to the acquisition context. The second issue concerns the location of structures to be characterized on the conductive tracks. Dealing with a large amount of redundancy and noise, a graph-matching method is presented—discriminating graph labels are developed to overcome the redundancy, while a flexible assignment optimizer solves the inexact matching arising from noises on graphs. The resulting automated location system brings reproducibility for secure characterization of integrated systems, besides accuracy and time speed increase.

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.

L’automatisation est un enjeu stratégique dans l’industrie. Dans le contexte très particulier de la caractérisation sécuritaire de circuits intégrés, nous proposons d’automatiser le déplacement du système optique d’un banc d’injection laser. À travers ce travail, notre objectif est de rendre possible l’automatisation de processus qui requièrent initialement l’intervention humaine. Nous proposons pour cela deux outils dédiés au domaine de la vision infrarouge de circuits intégrés. Dans un premier temps, un processus de mise au point automatique du système optique est présenté, permettant de focaliser les pistes conductrices du circuit intégré, selon des critères propres au contexte. Deux approches sont mises en place, fondées sur l’analyse des images dans le domaine temps-fréquence : une approche passe par une transformée en ondelettes des images, l’autre passe par une transformée polynomiale. Si elles sont toutes deux optimisées pour l’analyse d’images infrarouges, les autofocus qui en découlent ont chacun leur avantage : temps d’exécution pour l’un, précision pour l’autre. Dans un second temps, nous présentons un système de localisation de structures électroniques. Ce système met en œuvre des appariements de graphes et fait intervenir des descripteurs décisifs pour notre application. Dans notre contexte, outre sa robustesse, notre approche tire profit des graphes dans la reconnaissance de formes à partir de simples schématisations. Le déploiement de ces outils offre des perspectives majeures pour l’amélioration des caractérisations sécuritaires, pour lesquelles la vision par ordinateur est l’élément clé.