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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.

A human gesture prediction system can be used to estimate human gestures in advance of the actual action to reduce delays in interactive systems. Hand gestures are particularly necessary for human⁻computer interaction. Therefore, the gesture prediction system must be able to capture hand movements that are both complex and quick. We have already reported a method that allows strain sensors and wearable devices to be fabricated in a simple and easy manner using pyrolytic graphite sheets (PGSs). The wearable electronics could detect various types of human gestures with high sensitivity, high durability, and fast response. In this study, we demonstrated hand gesture prediction by artificial neural networks (ANNs) using gesture data obtained from data gloves based on PGSs. Our experiments entailed measuring the hand gestures of subjects for learning purposes and we used these data to create four-layered ANNs, which enabled the proposed system to successfully predict hand gestures in real time. A comparison of the proposed method with other algorithms using temporal data analysis suggested that the hand gesture prediction system using ANNs would be able to forecast various types of hand gestures using resistance data obtained from wearable devices based on PGSs.

The nanoscale heat dissipation (Joule heating) and mass transport during electromigration (EM) have attracted considerable attention in recent years. Here, the EM-driven movement of voids in gold (Au) nanowires of different shapes (width range: 50⁻300 nm) was directly observed by performing atomic force microscopy. Using the data, we determined the average mass transport rate to be 10⁵ to 10⁶ atoms/s. We investigated the heat dissipation in L-shaped, straight-shaped, and bowtie-shaped nanowires. The maximum Joule heating power of the straight-shaped nanowires was three times that of the bowtie-shaped nanowires, indicating that EM in the latter can be triggered by lower power. Based on the power dissipated by the nanowires, the local temperature during EM was estimated. Both the local temperature and junction voltage of the bowtie-shaped nanowires increased with the decrease in the Joule heating power and current, while the current density remained in the order of 10⁸ A/cm². The straight-shaped nanowires exhibited the same tendency. The local temperature at each feedback point could be simply estimated using the diffusive heat transport relationship. These results suggest that the EM-driven mass transport can be controlled at temperatures much lower than the melting point of Au., Competing Interests: The authors declare no conflict of interest.