Information theoretic study on x-ray imaging of integrated circuits in a low photon budget

Physics-aware machine learning (PAML) has proven to be successful in re- trieving object in a low-photon imaging condition. In this study, we take an information theoretic approach to study maximum amount of information (i.e., the imaging capacity) that can be reliably retrieved under photon-starved imaging condition. We formulate a sim- ple problem to derive the imaging capacity using Monte Carlo approach: small integrated circuit objects (made out of two different type of materials) imaged with monochromatic x-rays. The simple problem is useful in that the entropies for priors and measurements can be explicitly computed. It is a simple version of the imaging system proposed in Ref. [3]. The constructed problem will help us understand the limit on photon budget, information gain associated with varying number of scan angles, and optimal PAML algorithm that can approach the theoretic imaging capacity.