Three-dimensional vertical structural electrochemical random access memory for high-density integrated synapse device.

Three-terminal (3T) structured electrochemical random access memory (ECRAM) has been proposed as a synaptic device based on improved synaptic characteristics. However, the proposed 3T ECRAM has a larger area requirement than 2T synaptic devices; thereby limiting integration density. To overcome this limitation, this study presents the development of a high-density vertical structure for the 3T ECRAM. In addition, complementary metal-oxide semiconductor (CMOS)-compatible materials and 8-inch wafer-based CMOS fabrication processes were utilized to verify the feasibility of mass production. The achievements of this work demonstrate the potential for high-density integration and mass production of 3T ECRAM devices.

Ag-Ion-Based Transparent Threshold Switching Selector with Filament-Size-Dependent Rectifying Behavior.

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.

Solid state thin electrolyte to overcome transparency-capacity dilemma of transparent supercapacitor.

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.

Novel training method for metal-oxide memristive synapse device to overcome trade-off between linearity and dynamic range.

Synapse devices are essential for the hardware implementation of neuromorphic computing systems. However, it is difficult to realize ideal synapse devices because of issues such as nonlinear conductance change (linearity) and a small number of conductance states (dynamic range). In this study, the correlation between the linearity and dynamic range was investigated. Consequently, we found a trade-off relationship between the linearity and dynamic range and proposed a novel training method to overcome this trade-off.

Ion-Driven Electrochemical Random-Access Memory-Based Synaptic Devices for Neuromorphic Computing Systems: A Mini-Review.

To enhance the computing efficiency in a neuromorphic architecture, it is important to develop suitable memory devices that can emulate the role of biological synapses. More specifically, not only are multiple conductance states needed to be achieved in the memory but each state is also analogously adjusted by consecutive identical pulses. Recently, electrochemical random-access memory (ECRAM) has been dedicatedly designed to realize the desired synaptic characteristics. Electric-field-driven ion motion through various electrolytes enables the conductance of the ECRAM to be analogously modulated, resulting in a linear and symmetric response. Therefore, the aim of this study is to review recent advances in ECRAM technology from the material and device engineering perspectives. Since controllable mobile ions play an important role in achieving synaptic behavior, the prospect and challenges of ECRAM devices classified according to mobile ion species are discussed.

Alternative negative weight for simpler hardware implementation of synapse device based neuromorphic system.

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.