This analysis demonstrates reproducibility in positron annihilation modeling, indicating significant advancements in defect characterization methods using Python.
We present a method for solving the positron diffusion equation in multi-layer systems. Our approach incorporates material-specific implantation profiles, diffusion parameters, and positron affinities. It utilizes a Markov chain approach to model annihilation probabilities and provides fitting capabilities for experimental S (lineshape) parameter data. We have implemented this algorithm in Python and made it available for free under the name LIMPID. To demonstrate its performance, we analyze depth-resolved Doppler-Broadening Spectroscopy measurements of a Cu layer on a Si substrate, achieving excellent agreement with the experimental profiles. The LIMPID tool enhances the reproducibility and comparability of positron defect characterization measurements across different research groups.
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Mathes et al. (2025) studied this question.
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