Abstract
This paper summarizes approaches to image quality prediction in support of an effort under the IARPA RAVEN program to demonstrate a non-destructive, tabletop X-ray microscope for high-resolution 3D imaging of integrated circuits (ICs). The fluorescent X-rays are generated by scanning an electron beam along an appropriately patterned target layer placed in front of the sample and are then detected after passing through the sample by a high-resolution (in both solid angle and energy) backside sensor array. The images are created by way of a model-based tomographic inversion algorithm, with image resolution depending critically on the electron beam scan density and diversity of sample orientations. We derive image quality metrics that quantify the image point spread function and noise sensitivity for any proposed experiment design. Application of these metrics will guide final system design when physical data are not yet available.
Highlights
Image quality (IQ) assessment is an important element of system design, optimization, and quality control [1, 2]
Prospective image quality prediction is based on a highquality end-to-end simulation of the system data product, and subsequent image reconstruction algorithm applied to that data
Given an accurate system forward model, IQ assessment is relatively straightforward in cases of “direct” image reconstruction algorithms, in which a relatively simple forward algorithm is applied to the data to obtain an image
Summary
Image quality (IQ) assessment is an important element of system design, optimization, and quality control [1, 2]. A complete assessment evaluates the entire imaging chain including both data acquisition and image reconstruction stages. Given an accurate system forward model, IQ assessment is relatively straightforward in cases of “direct” image reconstruction algorithms, in which a relatively simple (though perhaps numerically intensive) forward algorithm is applied to the data to obtain an image. Less straightforward are imaging algorithms that rely on model-based iterative reconstruction (MBIR). The resulting algorithm may be highly nonlinear in the data, and IQ metrics may vary strongly with position, noise levels, source and detector blur, etc. This certainly increases the complexity of, and necessity for, IQ assessment. Specific illustrative applications to the RAVEN IC tomography problem will be presented separately
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