Abstract
Feedback-controlled electromigration (FCE) is employed to control metal nanowires with quantized conductance and create nanogaps and atomic junctions. In the FCE method, the experimental parameters are commonly selected based on experience. However, optimization of the parameters by way of tuning is intractable because of the impossibility of attempting all different combinations systematically. Therefore, we propose the use of the Ising spin model to optimize the FCE parameters, because this approach can search for a global optimum in a multidimensional solution space within a short calculation time. The FCE parameters were determined by using the energy convergence properties of the Ising spin model. We tested these parameters in actual FCE experiments, and we demonstrated that the Ising spin model could improve the controllability of the quantized conductance in atomic junctions. This result implies that the proposed method is an effective tool for the optimization of the FCE process in which an intelligent machine can conduct the research instead of humans.
Highlights
Feedback-controlled electromigration (FCE) is employed to control metal nanowires with quantized conductance and create nanogaps and atomic junctions
The Ising spin model can be applied to many combinatorial optimization problems[17], and it can determine the optimal solution from large numbers of candidate solutions within short calculation times[18]
The local minimum solutions obtained by Ising spin computing are considered suitable for use in the FCE method. These results show that Ising spin computing could discover a remarkable amount of information about the FCE by starting from disorderly generated data; that is, these are the results of applying machine learning to disordered data to obtain order in exploring the experimental parameters of the FCE method
Summary
Feedback-controlled electromigration (FCE) is employed to control metal nanowires with quantized conductance and create nanogaps and atomic junctions. We tested these parameters in actual FCE experiments, and we demonstrated that the Ising spin model could improve the controllability of the quantized conductance in atomic junctions This result implies that the proposed method is an effective tool for the optimization of the FCE process in which an intelligent machine can conduct the research instead of humans. Our approach opens new avenues for bridging the gap between key problems of parameter optimization in fabricating nanoscale devices and their implementation to Ising spin computing. We applied an optimum VFB schedule obtained by the ground-state searches of the Ising spin model to the FCE experiments to confirm the usefulness of our system (Fig. 1d).
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