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Abstract This study investigates the impact of an oxygen reservoir layer on the performance of three‐terminal (3T) oxide ion‐based electrochemical random access memory (Ox‐ECRAM). Three Ox‐ECRAM synapse devices are compared: single layer, double 1, and double 2. The results indicate that the oxygen reservoir layer is crucial for maintaining channel conductance while preventing gate leakage. The oxygen reservoir layer also modulates conductance via the absorption and supply of oxygen ions. In addition, the ion migration barrier energy of the oxygen reservoir layer plays a key role in the reliability of the Ox‐ECRAM, with higher values leading to more stable retention and endurance. The results of this study highlight the importance of the oxygen reservoir layer in Ox‐ECRAM performance.

A synaptic device with a multilayer structure is proposed to reduce the operating power of neuromorphic computing systems while maintaining a high-density integration. A simple metal–insulator–metal (MIM)-structured multilayer synaptic device is developed using an 8-inch wafer-based and complementary metal–oxide–semiconductor (CMOS) fabrication process. The three types of MIM-structured synaptic devices are compared to assess their effects on reducing the operating power. The obtained results exhibited low-power operation owing to the inserted layers acting as an internal resistor. The modulated operational conductance level and simple MIM structure demonstrate the feasibility of implementing both low-power operation and high-density integration in multilayer synaptic devices.

Abstract For portable and transparent electronic applications, transparent supercapacitor (T-SC) is developed to act as an energy storing device. Because electric and optical characteristics of the supercapacitor are strongly dependent on its thickness, all solid state T-SC was developed based on sensitively controllable fabrication process. We were able to attain an optimum thickness for the T-SC such that it exhibited an excellent transparency as well as capacity. Thus, the transparency-capacity dilemma, that is, the thickness of a T-SC increases with respect to its capacity while it is inversely proportional to its transparency, was solved through our proposed T-SC structure. Consequently, more than 60% transparency and 80% capacitance retention of 1500 charge/discharge cycles were achieved. The overcoming of transparency-capacity dilemma can enhance the T-SC applicability as a core energy storage device.

Abstract Lately, there has been a rapid increase in the use of software-based deep learning neural networks (S-DNN) for the analysis of unstructured data consumption. For implementation of the S-DNN, synapse-device-based hardware DNN (H-DNN) has been proposed as an alternative to typical Von-Neumann structural computing systems. In the H-DNN, various numerical values such as the synaptic weight, activation function, and etc., have to be realized through electrical device or circuit. Among them, the synaptic weight that should have both positive and negative numerical values needs to be implemented in a simpler way. Because the synaptic weight has been expressed by conductance value of the synapse device, it always has a positive value. Therefore, typically, a pair of synapse devices is required to realize the negative weight values, which leads to additional hardware resources such as more devices, higher power consumption, larger area, and increased circuit complexity. Herein, we propose an alternative simpler method to realize the negative weight (named weight shifter) and its hardware implementation. To demonstrate the weight shifter, we investigated its theoretical, numerical, and circuit-related aspects, following which the H-DNN circuit was successfully implemented on a printed circuit board.

A metal–insulator–metal-structured Ag-filament-based transparent threshold switch is developed as a selector device for a crossbar array, which can lead to high-density integration of advanced memory devices. Both threshold switching and rectifying behavior were achieved based on sensitive control of the filament size. Conduction mechanism analyses demonstrated that the rectifying behavior resulted from the Schottky barrier at the interface. From the threshold switching, including the rectifying behavior, the available crossbar array size is 105-times larger.