The threat of hardware Trojans has been widely recognized by academia, industry, and government agencies. A Trojan can compromise security of a system in spite of cryptographic protection. The damage caused by a Trojan may not be limited to a business or reputation, but could have a severe impact on public safety, national economy, or national security. An extremely stealthy way of implementing hardware Trojans has been presented by at CHES'2012. Their work have shown that it is possible to inject a Trojan in a random number generator compliant with FIPS 140-2 and NIST SP800-90 standards by exploiting non-zero aliasing probability of Logic Built-In-Self-Test (LBIST). In this paper, we present two methods for modifying LBIST to prevent such an attack. The first method makes test patterns dependent on a configurable key which is programed into a chip after the manufacturing stage. The second method uses a remote test management system which can execute LBIST using a different set of test patterns at each test cycle.
Changes in met al wires and transistors can typically be detected with a high probability. It is more difficult to detect changes made to the dopant, especially in nanoscale technologies. To detect changes in dopant types, in addition to all met al layers of a chip, the contact layer has to be examined. Imaging of a contact layer requires a higher magnification compared to the one of met al layers. According to @cite_33 high-quality imaging of the contact layer is 16 times more costly than imaging of a met al layer.
@cite_33
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A successful detection of the stealthy dopant-level circuit trojan, proposed by at CHES 2013 [1], is reported. Contrary to an assumption made by , dopant types in active region are visible with either scanning electron microscopy SEM or focused ion beam FIB imaging. The successful measurement is explained by an LSI failure analysis technique called the passive voltage contrast [2]. The experiments are conducted by measuring a dedicated chip. The chip uses the diffusion programmable device [3]: an anti-reverse-engineering technique by the same principle as the stealthy dopant-level trojan. The chip is delayered down to the contact layer, and images are taken with 1 an optical microscope, 2 SEM, and 3 FIB. As a result, the four possible dopant-well combinations, namely i p+ n-well, ii p+ p-well, iii n+ n-well and iv n+ p-well are distinguishable in the SEM images. Partial but sufficient detection is also achieved with FIB. Although the stealthy dopant-level circuits are visible, however, they potentially make a detection harder. That is because the contact layer should be measured. We show that imaging the contact layer is at most 16-times expensive than that of a met al layer in terms of the number of images. In the presence of met al implants, met al artifacts are introduced to x-ray computed tomography CT images. Although a large number of met al artifact reduction (MAR) methods have been proposed in the past decades, MAR is still one of the major problems in clinical x-ray CT. In this paper, we develop a convolutional neural network (CNN)-based open MAR framework, which fuses the information from the original and corrected images to suppress artifacts. The proposed approach consists of two phases. In the CNN training phase, we build a database consisting of met al-free, met al-inserted and pre-corrected CT images, and image patches are extracted and used for CNN training. In the MAR phase, the uncorrected and pre-corrected images are used as the input of the trained CNN to generate a CNN image with reduced artifacts. To further reduce the remaining artifacts, water equivalent tissues in a CNN image are set to a uniform value to yield a CNN prior, whose forward projections are used to replace the met al-affected projections, followed by the FBP reconstruction. The effectiveness of the proposed method is validated on both simulated and real data. Experimental results demonstrate the superior MAR capability of the proposed method to its competitors in terms of artifact suppression and preservation of anatomical structures in the vicinity of met al implants. A bipolar transistor (10) in an IC includes a semiconductor wafer defining a collector area (14) with a first conductivity type, a base area (20) with a second conductivity type formed in the collector area (14), and an emitter formed in the base area. A field oxide is positioned on the surface of the semiconductor wafer surrounding the emitter (30) and substantially covering the base area (20) and an implant of the second conductivity type is positioned in the base area (20) between and spaced from the emitter (30) and the outer periphery of the base area (20). The implant further has a heavier concentration of the second conductivity type than the base area to compensate for loss of the second conductivity type under the field oxide and to separate the transistor current path from the breakdown path, which improves the collector to emitter breakdown voltage (BVCEO) while still maintaining a high beta. In dual energy computerized tomography (DECT) which is widely used in industrial areas and security inspection, met al artifact reduction (MAR) is a troublesome problem. Pronounced streaks appear in the atomic number reconstruction and the value appears to be highly inaccurate when met al objects are present. In this article, a practical MAR method for DECT is proposed. Firstly, sinogram segmentation based on active contour model is implemented to obtain the met al projection region (MPR). Then, TV inpainting for sinogram was applied before reconstruction. Experiments demonstrate that, with our MAR method, the accuracy and image quality of the atomic number can be greatly improved.