University of Florida Research Foundation Inc

Ronald Wilson

Damon Woodard

Domenic J Forte

Navid Asadizanjani

Navid Asadi-Zanjani

Mark M Tehranipoor

Nidish Vashistha

Hangwei Lu

Embodiments of the present disclosure provide methods, apparatus, systems, and computer program products for using an image of an integrated circuit (IC) including a plurality of cells to locate one or more target cells within the IC. Accordingly, in various embodiments, a footprint for each cell of the plurality of cells is encoded to transform the image of the IC into a two-dimensional string matrix. A string search algorithm is then applied on each encoded dopant region found in the two-dimensional string matrix using an encoded target layout cell to identify one or more candidate regions of interest within the image. Finally, a mask window is slid over each candidate region of interest while performing matching using match criteria to identify any target cells in the one or more target cells that are located within the candidate region of interest.

Knowledge-based object localization in images for hardware assurance

Various embodiments of the present disclosure provide for accelerated segmentation for reverse engineering of integrated circuits. In one example, an embodiment provides for receiving an SEM image for an integrated circuit, performing filtering and binarization with respect to the SEM image, extracting information associated with filter sizes for the filtering, extracting signatures related to a distribution for background pixels and foreground pixels of the SEM image, extracting respective distance to mean signatures for the background pixels and the foreground pixels, and segmenting the SEM image based at least in part on the filter sizes and the respective distance to mean signatures to generate a segmented image for the integrated circuit.

Accelerated segmentation for reverse engineering of integrated circuits

Systems and methods are configured to generate a frequency map representing a density of objects found in regions of a sample that may be used in setting parameters for imaging the regions. Various embodiments involve binarizing the pixels for a raw image of the sample to transform the image into binary data. Run-length encoded components are identified from the data for dimensions of the raw image. Each component is a length of a sequence of adjacent pixels found in a dimension with the same value in the binary data. A projection of the image is then generated from projection values for the dimensions. Each projection value provides a measure of the density of objects present in a dimension with respect to the components identified for the dimension. This projection is used to identify a level of density for each region of the sample from which the frequency map is generated.

Automatic sharpness adjustment for imaging modalities

A histogram-based method for auto segmentation of integrated circuit structures is disclosed. The method includes an auto-segmentation process/algorithm, which works on the histogram of the SEM image and does not try to model the noise sources or the features. The auto-segmentation process/algorithm extracts the number of peaks in the histogram from low magnification SEM images or SEM images not necessarily having high quality images, significantly simplifies the traditionally lengthy and expensive IC reverse engineering efforts. Hence, the size of the image does not affect the final segmentation. The auto-segmentation process/algorithm performs the steps of: extract a first histogram from the first SEM image; identifying boundaries of the plurality of structural elements in the IC based at least in part on an output of the first histogram; and auto-segmenting the first SEM image into the plurality of structural elements.

Histogram-based method for auto segmentation of integrated circuit structures from SEM images

Methods and apparatus are provided for automatically extracting standard cells to form a standard cell library using raw multi-layer images of an IC. Accordingly, various embodiments involve: extracting the raw contact layer image from the raw multi-layer images; binarizing the raw contact layer image to generate a binarized contact layer image identifying a plurality of contact rows and a plurality of contact columns; determining a plurality of Vlines based on a subset of the plurality of contact rows having a periodic nature; extracting a plurality of binarized contact layer image strips from the binarized contact layer image; encoding each binarized contact layer image strip using feature vectors and column distance values; applying a model rule set to each encoded binarized contact layer image strip for detecting cell boundaries; extracting the standard cells based on the cell boundaries; and storing the extracted cells to form a standard cell candidate library.

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Gainesville, FL, United States of America

Ronald Wilson