Feedback-controlled electromigration (FCE) has been employed to control metal nanowires with quantized conductance and to create nanogaps. The setting of the experimental parameters based on experiences is a common practice in FCE. However, tuning the optimization of parameters is intractable because trying all different combinations systematically is practically impossible. Therefore, we proposed an optimization process of the FCE parameters using Ising spin model, which can search for the global optimum in a multidimensional solution space within short calculation time. The FCE parameters were determined through a convergence property of the Ising spin model. This result implies that the proposed method is an effective tool for the process optimization of FCE.
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