An ionic electrochemical-reaction-based two-terminal synaptic device (Ionic Synapse) is developed for improved weight updating in hardware-based neuromorphic systems. The developed ionic synapse consists of an anode and a cathode that have the typical structure of a rechargeable battery. It can migrate metal ions from the anode (cathode) to the cathode (anode) under electrical bias, yielding total conductance changes. Analysis of the fabricated device reveals that the conductance changes are strongly dependent on the amounts of migrated ions, which can be sensitively controlled using applied identical pulses. Consequently, improved linearity for weight-updating is successfully achieved. These findings indicate the potential of the ionic electrochemical reaction mechanism for the realization of a desirable two-terminal synaptic device.

In this study, a highly reliable Li based two-terminal electrochemical synapse device was developed to investigate the synaptic characteristics. Based on the identified fabrication conditions, a solid-state electrolyte was interposed between the anode and cathode to prevent the self-injection phenomenon. Consequently, improved reliabilities, such as endurance and retention, were achieved. These results demonstrate the significant potential of two-terminal synaptic device in the hardware implementation of neuromorphic computing systems.

In this paper, we demonstrated the feasibility of the Aerosol Deposition (AD) method which can be adapted as a future fabrication process for flexible electronic devices. On the basis of this method’s noticeable advantages such as room-temperature processing, suitability for mass production, wide material selectivity, and direct fabrication on a flexible substrate, we fabricated and evaluated a flexible conductive bridge random access memory (CBRAM) to confirm the feasibility of this method. The CBRAM was fabricated by the AD-method, and a novel film formation mechanism was observed and analyzed. Considering that the analyzed film formation mechanism is notably different with previously reported for film formation mechanisms of the AD method, these results of study will provide strong guidance for the fabrication of flexible electronic device on ductile substrate.

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.

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.

In this paper, we demonstrated memory and energy storage dual operation in GeS2/Ag conducting bridge RAM. Both operations are based on electrochemical reactions and Ag diffusion in the GeS2 electrolyte. Depending on specific bias and current conditions, the same device can be used for data or energy storage: in memory mode, Ag-based filament is formed in GeS2, while in energy storage mode, Ag bulk diffusion is achieved. Voltage sweep and constant current stress cycling modes for energy storage are evaluated, and the total stored charge is quantified. These obtained results open the path for new autonomous or ultra-low power circuits, integrating dual operation devices composed of the same stack, where energy storage devices could provide energy to the memory array.