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In this article, the focus is on using machine learning methods to analyse non-volatile memory devices. This is important because the production of integrated circuits in the sub-micrometre range depends on advancements in manufacturing process technology, and it is crucial to evaluate how manufacturing tolerances affect the functionality of contemporary integrated circuits. Traditionally, Monte Carlo-based techniques have been used to accurately evaluate the impact of manufacturing tolerances on the functionality of integrated circuits. However, these techniques are computationally time-consuming. We will propose a scheme to "learn" the variation of the read margin (parallel and anti-parallel resistance) performance of spintronics devices. The machine learning approach, artificial neural network, is focused on this study (Read margin of spin transfer torque (STT)) spintronics devices. The accuracy for STT by …