The gap-type atomic switch is a novel neuromorphic device that possesses functions such as analog changes in resistance and short-term/long-term memory-based learning. However, it is difficult to integrate conventional gap-type atomic switches that use a vacuum gap and Ag2+δS, which has restricted their practical use. In this study, we developed a new, easy to fabricate gap-type atomic switch that incorporates a molecular layer as a gap and Ta2O5 as an ionic transfer material. This molecular gap-type atomic switch operates in a manner that is similar to conventional vacuum gap-type atomic switches. We also demonstrate stochastic operations using the aforementioned molecular gap-type atomic switches. These results indicate a higher potential for the practical use of gap-type atomic switches.

Learning by human beings is achieved by changing the synaptic weights of a neural network in the brain. Low frequency stimulation temporarily increases a synaptic weight, which then decreases to the initial low state in the interval after each stimulation. Conversely, high frequency stimulation keeps a synaptic weight at an elevated level, even after the stimulation ends. These phenomena are termed short‐term plasticity (STP) and long‐term potentiation (LTP), respectively. These functions have been emulated by various nonvolatile devices, with the aim of developing hardware‐based artificial intelligent (AI) systems. In order to use the functions in actual AI systems with other conventional devices, control of the operating characteristics, such as matching a decay constant in STP, is indispensable. This paper reports an electrochemical method for controlling the characteristics of time‐dependent neuromorphic operations of molecular gap atomic switches. Pre‐doping of Ag+ cations into an ionic transfer layer (Ta2O5) changes the amount of shift in an electrochemical potential in the time‐dependent operation, which drastically improves the decaying characteristics in STP mode.

This study investigates vehicle operating time as a constraint within the vehicle routing problem (VRP) with multiple trips (VRPMT). In the basic VRP, a single route is assigned to each vehicle and the solution must satisfy both the load and distance (or travel time) constraints. However, in the real-world problem, one vehicle is assigned to multiple routes per day. Since this becomes a large-scale combinatorial optimization problem, it becomes difficult to find an exact solution. Therefore, in previous research, a heuristic method using a two-phase algorithm was proposed. However, since the precision of the two-phase algorithm is greatly influenced by the solution selected for the first phase, selection of solutions in this phase is crucial. In this study, the advantages of the one-and two-phase methods are integrated in a new proposed method.